From eddf2b2142b5559fff754e8587400c395f6e0334 Mon Sep 17 00:00:00 2001 From: Florian Mai Date: Sun, 11 Jun 2017 19:10:28 +0200 Subject: [PATCH 001/116] Read data from csv format, consider extra samples. --- Code/lucid_ml/run.py | 30 ++++++++- Code/lucid_ml/utils/Extractor.py | 103 +++++++++++++++++++++++++------ 2 files changed, 112 insertions(+), 21 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 755a5f4..2242d5f 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -59,7 +59,10 @@ def run(options): if VERBOSE: print("Y = " + str(Y.shape)) else: # --- LOAD DATA --- - X_raw, Y_raw, tr = load_dataset(DATA_PATHS, options.data_key, options.fulltext) + if options.fixed_folds: + X_raw, Y_raw, tr, fold_list = load_dataset(DATA_PATHS, options.data_key, options.fulltext, fixed_folds=True) + else: + X_raw, Y_raw, tr = load_dataset(DATA_PATHS, options.data_key, options.fulltext, fixed_folds=False) if options.toy_size < 1: if VERBOSE: print("Just toying with %d%% of the data." % (options.toy_size * 100)) zipped = list(zip(X_raw, Y_raw)) @@ -181,6 +184,25 @@ def run(options): scores = defaultdict(list) if options.cross_validation: kf = cross_validation.KFold(X.shape[0], n_folds=options.folds, shuffle=True) + elif options.fixed_folds: + kf = [] + basic_folds = range(options.folds) + + # we assume the extra data to be in the last fold + extra_data = [index for index,x in enumerate(fold_list) if x == options.folds] + + for i in range(options.folds): + + training_fold = [index for index,x in enumerate(fold_list) if x in basic_folds and x != i] + + # add more training data from extra samples + if options.extra_samples_factor > 1: + num_extra_samples = int(min((1 - options.extra_samples_factor) * len(training_fold), len(extra_data))) + training_fold += extra_data[:num_extra_samples] + + test_fold = [index for index,x in enumerate(fold_list) if x == i] + kf.append((training_fold, test_fold)) + else: kf = ShuffleSplit(X.shape[0], test_size=options.test_size, n_iter=1) for train, test in kf: @@ -369,6 +391,8 @@ def _generate_parsers(): "Run on one fold [False]") execution_options.add_argument('-X', action="store_true", dest="cross_validation", default=False, help= "Perform cross validation [False]") + execution_options.add_argument('--fixed_folds', action="store_true", dest="fixed_folds", default=False, help= + "Perform cross validation with fixed folds.") execution_options.add_argument('-i', '--interactive', action="store_true", dest="interactive", default=False, help= \ "Use whole supplied data as training set and classify new inputs from STDIN") @@ -381,6 +405,10 @@ def _generate_parsers(): "Number of folds used for cross validation [10]") detailed_options.add_argument('--toy', type=float, dest='toy_size', default=1.0, help= "Eventually use a smaller block of the data set from the very beginning. [1.0]") + detailed_options.add_argument('--extra_samples_factor', type=float, dest='extra_samples_factor', default=1.0, help= + "This option only has an effect when the '--fixed_folds' option is true. The value determines the factor 'x >= 1' by which\ + the training set is enriched with samples from the 11th fold. Hence, the total number of training data will be \ + x * size of tranining set. By default, the value is x = 1.") detailed_options.add_argument('--training-error', action="store_true", dest="training_error", default=False, help=\ "Compute training error") diff --git a/Code/lucid_ml/utils/Extractor.py b/Code/lucid_ml/utils/Extractor.py index 43dcece..698786e 100644 --- a/Code/lucid_ml/utils/Extractor.py +++ b/Code/lucid_ml/utils/Extractor.py @@ -3,22 +3,85 @@ import json import os import random +import pandas as pd +import math from utils.thesaurus_reader import ThesaurusReader -# paths to required resources: -# [0: titles/documents, 1: gold, 2: thesaurus] -def load_dataset(DATA_PATHS, key='econ62k', fulltext=False): +def split_labels(labels_string): + return labels_string.split(",") + +def load_data(df, fulltext, fixed_folds): + if fulltext: - data = load_documents(DATA_PATHS[key]['X']) + content_column = "fulltext_path" else: - data = load_titles(DATA_PATHS[key]['X']) - gold = load_gold(DATA_PATHS[key]['y']) - data_list, gold_list = reduce_dicts(data, gold) - tr = ThesaurusReader(DATA_PATHS[key]['thes']) + content_column = "title" + + # bring title/fulltext in dictionary format + content = dict() + for row in df.iterrows(): + content[row[1].loc["id"]] = row[1][content_column] + + # bring goldstandard in dictionary format + gold = dict() + for row in df.iterrows(): + gold[row[1]["id"]] = split_labels(row[1]["labels"]) + + # by default, there is only one fold + folds = dict() + folds.update({key : 0 for key in gold}) + if fixed_folds: + for row in df.iterrows(): + folds[row[1]["id"]] = int(row[1]["fold"]) + + data_list, gold_list, fold_list = reduce_dicts([content, gold, folds]) + + print(len(data_list)) + print(data_list) + if fulltext: + print(type(data_list[0])) + fulltext_indices = [index for index, x in enumerate(data_list) if type(x) == str or not math.isnan(x)] + + def elems_by_indices(some_list): + return [some_list[i] for i in fulltext_indices] + + data_list, gold_list, fold_list = elems_by_indices(data_list), elems_by_indices(gold_list), elems_by_indices(fold_list) + print(len(data_list)) + + return data_list, gold_list, fold_list + +# paths to required resources: +# [0: titles/documents, 1: gold, 2: thesaurus] +def load_dataset(DATA_PATHS, key='econ62k', fulltext=False, fixed_folds = False): + data_set = DATA_PATHS[key] + + dataset_format = data_set["format"] if "format" in data_set else "separate" + + print("Format is", dataset_format) - return data_list, gold_list, tr + if dataset_format == "separate": + + if fulltext: + data = load_documents(DATA_PATHS[key]['X']) + else: + data = load_titles(DATA_PATHS[key]['X']) + gold = load_gold(DATA_PATHS[key]['y']) + data_list, gold_list = reduce_dicts(data, gold) + tr = ThesaurusReader(DATA_PATHS[key]['thes']) + return data_list, gold_list, tr + + elif dataset_format == "combined": + # extract the available folds and keep each folds samples in a separate list + df = pd.read_csv(data_set["X"]) + data_list, gold_list, fold_list = load_data(df, fulltext, fixed_folds) + tr = None if "thes" not in data_set else ThesaurusReader(data_set['thes']) + return data_list, gold_list, tr, fold_list + + else: + print("Format not recognized:", dataset_format) + raise ValueError("No such format: " + dataset_format) # expected format: \t def load_titles(titles_path): @@ -57,21 +120,21 @@ def load_gold(path): return gold -def reduce_dicts(titles, gold, shuffle=False): - """ reduce 2 dictionaries to 2 lists, +def reduce_dicts(dicts, shuffle=False): + """ reduces a list of dictionaries to a list of lists, providing 'same index' iff 'same key in the dictionary' """ - titles, gold = dict(titles), dict(gold) - titles_list = [] - gold_list = [] - for key in titles: - titles_list.append(titles[key]) - gold_list.append(gold[key]) + dicts = list(map(dict, dicts)) + lists = [[] for i in range(len(dicts))] + + for key in dicts[0]: + for i, some_dict in enumerate(dicts): + lists[i].append(some_dict[key]) if shuffle: # this is the only way to shuffle them - zipped = list(zip(titles_list, gold_list)) + zipped = list(zip(lists)) random.shuffle(zipped) - titles_list, gold_list = zip(*zipped) + lists = zip(*zipped) - return titles_list, gold_list + return tuple(lists) From d3ed0dacade110a2c60281ff2626976abbf56d82 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 11 Jun 2017 21:35:33 +0200 Subject: [PATCH 002/116] Removed some unnecessary printouts --- Code/lucid_ml/utils/Extractor.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/Code/lucid_ml/utils/Extractor.py b/Code/lucid_ml/utils/Extractor.py index 698786e..32a9499 100644 --- a/Code/lucid_ml/utils/Extractor.py +++ b/Code/lucid_ml/utils/Extractor.py @@ -37,10 +37,7 @@ def load_data(df, fulltext, fixed_folds): data_list, gold_list, fold_list = reduce_dicts([content, gold, folds]) - print(len(data_list)) - print(data_list) if fulltext: - print(type(data_list[0])) fulltext_indices = [index for index, x in enumerate(data_list) if type(x) == str or not math.isnan(x)] def elems_by_indices(some_list): @@ -58,8 +55,6 @@ def load_dataset(DATA_PATHS, key='econ62k', fulltext=False, fixed_folds = False) dataset_format = data_set["format"] if "format" in data_set else "separate" - print("Format is", dataset_format) - if dataset_format == "separate": if fulltext: From 04cf27b130a46f399de26891baaf90764f0ce6e8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 11 Jun 2017 22:02:35 +0200 Subject: [PATCH 003/116] Max features and bugfix --- Code/lucid_ml/run.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 2242d5f..c0aa350 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -84,7 +84,7 @@ def run(options): persist_dir=options.persist_to, repersist=options.repersist, file_path=DATA_PATHS[options.data_key]['X']).analyze terms = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, - token_pattern=word_regexp) + token_pattern=word_regexp, max_features=options.max_features) concepts = CountVectorizer(input=input_format, analyzer=concept_analyzer, binary=options.binary, vocabulary=tr.nodename_index if not options.synsets else None) @@ -197,7 +197,7 @@ def run(options): # add more training data from extra samples if options.extra_samples_factor > 1: - num_extra_samples = int(min((1 - options.extra_samples_factor) * len(training_fold), len(extra_data))) + num_extra_samples = int(min((options.extra_samples_factor - 1) * len(training_fold), len(extra_data))) training_fold += extra_data[:num_extra_samples] test_fold = [index for index,x in enumerate(fold_list) if x == i] @@ -427,6 +427,8 @@ def _generate_parsers(): "use concepts [False]") feature_options.add_argument('-t', '--terms', action="store_true", dest="terms", default=False, help= \ "use terms [True]") + feature_options.add_argument('--max_features', type=int, dest="max_features", default=None, help= \ + "Specify the maximal number of features to be considered for a BoW model [None, i.e., infinity]") feature_options.add_argument('-s', '--synsets', action="store_true", dest="synsets", default=False, help= \ "use synsets [False]") feature_options.add_argument('-g', '--graphscoring', dest="graph_scoring_method", type=str, default="", \ From 23f7f0f1633c3afe6f80ed3b2d2d55e2005e3527 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 13 Jun 2017 12:00:48 +0200 Subject: [PATCH 004/116] Specify batch size and adjust learning rate accordingly --- Code/lucid_ml/classifying/neural_net.py | 15 +++++++++++---- Code/lucid_ml/run.py | 6 ++++-- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/classifying/neural_net.py b/Code/lucid_ml/classifying/neural_net.py index 616826b..0d5b6cb 100644 --- a/Code/lucid_ml/classifying/neural_net.py +++ b/Code/lucid_ml/classifying/neural_net.py @@ -43,10 +43,17 @@ def _batch_generatorp(X, batch_size): class MLP(BaseEstimator): - def __init__(self, verbose=0, model=None, final_activation='sigmoid'): + def __init__(self, verbose=0, model=None, final_activation='sigmoid', batch_size = 512): self.verbose = verbose self.model = model self.final_activation = final_activation + self.batch_size = batch_size + + # we scale the learning rate proportionally with the batch size as suggested by + # [Thomas M. Breuel, 2015, The Effects of Hyperparameters on SGD + # Training of Neural Networks] + # we found lr=0.01 to be a good learning rate for batch size 512 + self.lr = self.batch_size / 512 * 0.01 def fit(self, X, y): if not self.model: @@ -56,8 +63,8 @@ def fit(self, X, y): self.model.add(Dropout(0.5)) self.model.add(Dense(y.shape[1])) self.model.add(Activation(self.final_activation)) - self.model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.01)) - self.model.fit_generator(generator=_batch_generator(X, y, 256, True), + self.model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=self.lr)) + self.model.fit_generator(generator=_batch_generator(X, y, self.batch_size, True), samples_per_epoch=X.shape[0], nb_epoch=20, verbose=self.verbose) def predict(self, X): @@ -65,7 +72,7 @@ def predict(self, X): return sparse.csr_matrix(pred > 0.2) def predict_proba(self, X): - pred = self.model.predict_generator(generator=_batch_generatorp(X, 512), val_samples=X.shape[0]) + pred = self.model.predict_generator(generator=_batch_generatorp(X, self.batch_size), val_samples=X.shape[0]) return pred diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index c0aa350..34596c9 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -308,7 +308,7 @@ def create_classifier(options, num_concepts): l2r_metric = options.l2r_metric + "@20", n_jobs = options.jobs, translation_probability = options.translation_prob) - mlp = MLP(verbose=options.verbose) + mlp = MLP(verbose=options.verbose, batch_size = options.batch_size) classifiers = { "nn": NearestNeighbor(use_lsh_forest=options.lshf), "brknna": BRKNeighborsClassifier(mode='a', n_neighbors=options.k, use_lsh_forest=options.lshf, @@ -330,7 +330,7 @@ def create_classifier(options, num_concepts): "rocchiodt": ClassifierStack(base_classifier=RocchioClassifier(metric = 'cosine'), n_jobs=options.jobs, n=options.k), "logregressdt": ClassifierStack(base_classifier=logregress, n_jobs=options.jobs, n=options.k), "mlp": mlp, - "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid'), alpha=options.alpha, stepsize=0.01, verbose=options.verbose), + "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size), alpha=options.alpha, stepsize=0.01, verbose=options.verbose), "mlpthr": LinRegStack(mlp, verbose=options.verbose), "mlpdt" : ClassifierStack(base_classifier=mlp, n_jobs=options.jobs, n=options.k) } @@ -478,6 +478,8 @@ def _generate_parsers(): "Whether the ClassifierStack should make use of all label information and thus take into account possible interdependencies.") classifier_options.add_argument('--l2r-neighbors', dest="l2r_neighbors", type=int, default=45, help= "Specify n_neighbors argument for KneighborsL2RClassifier.") + classifier_options.add_argument('--batch_size', dest="batch_size", type=int, default=256, help= + "Specify batch size for neural network training.") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 5bd3286205f8ea30d85966cc3fe8d70010e74728 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 13 Jun 2017 12:33:04 +0200 Subject: [PATCH 005/116] Move to keras 2 --- Code/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/requirements.txt b/Code/requirements.txt index 6f13089..af65f9a 100644 --- a/Code/requirements.txt +++ b/Code/requirements.txt @@ -9,4 +9,4 @@ datrie decorator pandas tensorflow -keras==1.2 +keras From 9e2e197dd292869a7831d05afc63e9a7822f3396 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 13 Jun 2017 13:28:12 +0200 Subject: [PATCH 006/116] Fixed a bug occurring when reading data. --- Code/lucid_ml/utils/Extractor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/utils/Extractor.py b/Code/lucid_ml/utils/Extractor.py index 32a9499..048680d 100644 --- a/Code/lucid_ml/utils/Extractor.py +++ b/Code/lucid_ml/utils/Extractor.py @@ -62,7 +62,7 @@ def load_dataset(DATA_PATHS, key='econ62k', fulltext=False, fixed_folds = False) else: data = load_titles(DATA_PATHS[key]['X']) gold = load_gold(DATA_PATHS[key]['y']) - data_list, gold_list = reduce_dicts(data, gold) + data_list, gold_list = reduce_dicts([data, gold]) tr = ThesaurusReader(DATA_PATHS[key]['thes']) return data_list, gold_list, tr From 66ae8498588e92196eff17aa338c982b7879e337 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 14 Jun 2017 12:44:38 +0200 Subject: [PATCH 007/116] Move from deprecated sklearn.cross_validation to sklearn.model_selection package --- Code/lucid_ml/run.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 34596c9..776dc06 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -18,8 +18,7 @@ warnings.filterwarnings("ignore", category=UserWarning) -from sklearn import cross_validation -from sklearn.cross_validation import ShuffleSplit +from sklearn.model_selection import KFold, ShuffleSplit # from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer @@ -183,9 +182,10 @@ def run(options): # --- CROSS-VALIDATION --- scores = defaultdict(list) if options.cross_validation: - kf = cross_validation.KFold(X.shape[0], n_folds=options.folds, shuffle=True) + kf = KFold(n_splits=options.folds, shuffle=True) + print(kf.split(X)) elif options.fixed_folds: - kf = [] + fixed_folds = [] basic_folds = range(options.folds) # we assume the extra data to be in the last fold @@ -201,11 +201,18 @@ def run(options): training_fold += extra_data[:num_extra_samples] test_fold = [index for index,x in enumerate(fold_list) if x == i] - kf.append((training_fold, test_fold)) + fixed_folds.append((training_fold, test_fold)) + + class FixedFoldsGenerator(): + def split(self, X): + return fixed_folds + + kf = FixedFoldsGenerator() else: - kf = ShuffleSplit(X.shape[0], test_size=options.test_size, n_iter=1) - for train, test in kf: + kf = ShuffleSplit(test_size=options.test_size, n_splits = 1) + for train, test in kf.split(X): + if VERBOSE: print("=" * 80) X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test] From 6c7e22f6c025833260638d7a8fb10b96dc7e365e Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 15 Jun 2017 12:36:44 +0200 Subject: [PATCH 008/116] Enabled early stopping on validation set. --- Code/lucid_ml/classifying/neural_net.py | 39 +++++++++++++++-- Code/lucid_ml/file_paths.json | 12 ++++- Code/lucid_ml/run.py | 58 +++++++++++++++++++++---- 3 files changed, 96 insertions(+), 13 deletions(-) diff --git a/Code/lucid_ml/classifying/neural_net.py b/Code/lucid_ml/classifying/neural_net.py index 0d5b6cb..f565831 100644 --- a/Code/lucid_ml/classifying/neural_net.py +++ b/Code/lucid_ml/classifying/neural_net.py @@ -8,8 +8,29 @@ from keras.optimizers import Adam import numpy as np from sklearn.metrics import f1_score -from sklearn.linear_model import Ridge +from sklearn.linear_model import Ridge +from keras.callbacks import EarlyStopping +from keras.callbacks import Callback +from sklearn.metrics import f1_score +#=============================================================================== +# class EarlyStoppingBySklearnMetric(Callback): +# def __init__(self, metric=lambda y_test, y_pred : f1_score(y_test, y_pred, average='samples'), value=0.00001, verbose=0): +# super(Callback, self).__init__() +# self.metric = metric +# self.value = value +# self.verbose = verbose +# +# def on_epoch_end(self, epoch, logs={}): +# current = logs.get(self.monitor) +# if current is None: +# warnings.warn("Early stopping requires %s available!" % self.monitor, RuntimeWarning) +# +# if current < self.value: +# if self.verbose > 0: +# print("Epoch %05d: early stopping THR" % epoch) +# self.model.stop_training = True +#=============================================================================== def _batch_generator(X, y, batch_size, shuffle): number_of_batches = np.ceil(X.shape[0] / batch_size) @@ -48,6 +69,7 @@ def __init__(self, verbose=0, model=None, final_activation='sigmoid', batch_size self.model = model self.final_activation = final_activation self.batch_size = batch_size + self.validation_data_position = None # we scale the learning rate proportionally with the batch size as suggested by # [Thomas M. Breuel, 2015, The Effects of Hyperparameters on SGD @@ -64,8 +86,19 @@ def fit(self, X, y): self.model.add(Dense(y.shape[1])) self.model.add(Activation(self.final_activation)) self.model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=self.lr)) - self.model.fit_generator(generator=_batch_generator(X, y, self.batch_size, True), - samples_per_epoch=X.shape[0], nb_epoch=20, verbose=self.verbose) + + val_pos = self.validation_data_position + + callbacks = [] + if self.validation_data_position is not None: + callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')) + X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] + else: + X_train, y_train = X, y + self.model.fit_generator(generator=_batch_generator(X_train, y_train, self.batch_size, True), callbacks=callbacks, + samples_per_epoch=X.shape[0], nb_epoch=20, verbose=self.verbose, + validation_data = _batch_generator(X_val, y_val, self.batch_size, False) if self.validation_data_position is not None else None, + validation_steps = 10) def predict(self, X): pred = self.predict_proba(X) diff --git a/Code/lucid_ml/file_paths.json b/Code/lucid_ml/file_paths.json index b7d43ed..14b4ce2 100644 --- a/Code/lucid_ml/file_paths.json +++ b/Code/lucid_ml/file_paths.json @@ -8,5 +8,15 @@ "X": "../../Resources/example/example-fulltext", "y": "../../Resources/example/example-goldstandard.tsv", "thes": "../../Resources/example/stw.json" - } + }, + "econtitles": { + "format" : "combined", + "X" : "../../Resources/example/data_sample.csv", + "thes": "../../Resources/example/stw.json" + }, + "econtitles-full": { +"format" : "combined", +"X" : "../../../Economics/data_full.csv", +"thes": "../../Resources/example/stw.json" +} } diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 776dc06..5ca7492 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -7,6 +7,7 @@ from classifying.neural_net import MLP, ThresholdingPredictor from classifying.stack_lin_reg import LinRegStack +from rdflib.plugins.parsers.ntriples import validate os.environ['OMP_NUM_THREADS'] = '1' # For parallelization use n_jobs, this gives more control. import numpy as np @@ -45,6 +46,7 @@ from weighting.bm25transformer import BM25Transformer from weighting.concept_analysis import ConceptAnalyzer from weighting.graph_score_vectorizer import GraphVectorizer +import scipy.sparse as sps logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') @@ -183,7 +185,6 @@ def run(options): scores = defaultdict(list) if options.cross_validation: kf = KFold(n_splits=options.folds, shuffle=True) - print(kf.split(X)) elif options.fixed_folds: fixed_folds = [] basic_folds = range(options.folds) @@ -191,10 +192,17 @@ def run(options): # we assume the extra data to be in the last fold extra_data = [index for index,x in enumerate(fold_list) if x == options.folds] + validation_set_indices = [] for i in range(options.folds): training_fold = [index for index,x in enumerate(fold_list) if x in basic_folds and x != i] + if options.validation_size > 0: + # separate validation from training set here, and rejoin later if appropriate + num_validation_samples = int(len(training_fold) * options.validation_size) + validation_set_indices.append(training_fold[:num_validation_samples]) + training_fold = training_fold[num_validation_samples:] + # add more training data from extra samples if options.extra_samples_factor > 1: num_extra_samples = int(min((options.extra_samples_factor - 1) * len(training_fold), len(extra_data))) @@ -202,7 +210,8 @@ def run(options): test_fold = [index for index,x in enumerate(fold_list) if x == i] fixed_folds.append((training_fold, test_fold)) - + + # helper class to conform sklearn's model_selection structure class FixedFoldsGenerator(): def split(self, X): return fixed_folds @@ -211,16 +220,46 @@ def split(self, X): else: kf = ShuffleSplit(test_size=options.test_size, n_splits = 1) - for train, test in kf.split(X): + for iteration, (train, test) in enumerate(kf.split(X)): if VERBOSE: print("=" * 80) X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test] + + clf = create_classifier(options, Y_train.shape[1]) # --- INTERACTIVE MODE --- + + # extract a validation set and inform the classifier where to find it + if options.validation_size > 0: + # if we don't have fixed folds, we may pick the validation set randomly + if options.cross_validation or options.one_fold: + train, val = next(ShuffleSplit(test_size=options.validation_size, n_splits = 1).split(X_train)) + X_train, X_val, Y_train, Y_val = X_train[train], X_train[val], Y_train[train], Y_train[val] + elif options.fixed_folds: + X_train = X_train + X_val = X[validation_set_indices[iteration]] + Y_val = Y[validation_set_indices[iteration]] + + # put validation data at the end of training data and tell classifier the position where they start, if it is able + _, estimator = clf.steps[-1] + if hasattr(estimator, 'validation_data_position'): + estimator.validation_data_position = X_train.shape[0] + else: + raise ValueError("Validation size given although the estimator has no 'validation_data_position' property!") + + if sps.issparse(X): + X_train = sps.vstack((X_train, X_val)) + else: + X_train = np.vstack((X_train, X_val)) + + if sps.issparse(Y): + Y_train = sps.vstack((Y_train, Y_val)) + else: + Y_train = np.vstack((Y_train, Y_val)) # mlp doesn't seem to like being stuck into a new process... if options.debug or options.clf_key in {'mlp', 'mlpthr'}: - Y_pred, Y_train_pred = fit_predict(X_test, X_train, Y_train, options, tr) + Y_pred, Y_train_pred = fit_predict(X_test, X_train, Y_train, options, tr, clf) else: - Y_pred, Y_train_pred = fit_predict_new_process(X_test, X_train, Y_train, options, tr) + Y_pred, Y_train_pred = fit_predict_new_process(X_test, X_train, Y_train, options, tr, clf) if options.training_error: scores['train_f1_samples'].append(f1_score(Y_train, Y_train_pred, average='samples')) @@ -275,8 +314,7 @@ def split(self, X): return results -def fit_predict(X_test, X_train, Y_train, options, tr): - clf = create_classifier(options, Y_train.shape[1]) # --- INTERACTIVE MODE --- +def fit_predict(X_test, X_train, Y_train, options, tr, clf): if options.verbose: print("Fitting", X_train.shape[0], "samples...") clf.fit(X_train, Y_train) @@ -291,8 +329,8 @@ def fit_predict(X_test, X_train, Y_train, options, tr): return Y_pred, Y_pred_train @processify -def fit_predict_new_process(X_test, X_train, Y_train, options, tr): - return fit_predict(X_test, X_train, Y_train, options, tr) +def fit_predict_new_process(X_test, X_train, Y_train, options, tr, clf): + return fit_predict(X_test, X_train, Y_train, options, tr, clf) def create_classifier(options, num_concepts): # Learning 2 Rank algorithm name to ranklib identifier mapping @@ -408,6 +446,8 @@ def _generate_parsers(): detailed_options = parser.add_argument_group("Detailed Execution Options") detailed_options.add_argument('--test-size', type=float, dest='test_size', default=0.1, help= "Desired relative size for the test set [0.1]") + detailed_options.add_argument('--val-size', type=float, dest='validation_size', default=0., help= + "Desired relative size of the training set used as validation set [0.]") detailed_options.add_argument('--folds', type=int, dest='folds', default=10, help= "Number of folds used for cross validation [10]") detailed_options.add_argument('--toy', type=float, dest='toy_size', default=1.0, help= From 57234cad16b86aeaf8d90813a62bc6e902e4eab6 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 15 Jun 2017 16:59:43 +0200 Subject: [PATCH 009/116] Fixed bug in number of steps that occured when moving from keras1.2 to keras2 --- Code/lucid_ml/classifying/neural_net.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/classifying/neural_net.py b/Code/lucid_ml/classifying/neural_net.py index f565831..80be2a5 100644 --- a/Code/lucid_ml/classifying/neural_net.py +++ b/Code/lucid_ml/classifying/neural_net.py @@ -96,7 +96,7 @@ def fit(self, X, y): else: X_train, y_train = X, y self.model.fit_generator(generator=_batch_generator(X_train, y_train, self.batch_size, True), callbacks=callbacks, - samples_per_epoch=X.shape[0], nb_epoch=20, verbose=self.verbose, + steps_per_epoch=int(X.shape[0] / float(self.batch_size)) + 1, nb_epoch=20, verbose=self.verbose, validation_data = _batch_generator(X_val, y_val, self.batch_size, False) if self.validation_data_position is not None else None, validation_steps = 10) From a7a8022ab6ae813a7f17e8800ecb5608a2ed8fd8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 18 Jun 2017 10:21:40 +0200 Subject: [PATCH 010/116] Fixed a bug at prediction step where outdated keras functions are used --- Code/lucid_ml/classifying/neural_net.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/classifying/neural_net.py b/Code/lucid_ml/classifying/neural_net.py index 80be2a5..c310a72 100644 --- a/Code/lucid_ml/classifying/neural_net.py +++ b/Code/lucid_ml/classifying/neural_net.py @@ -105,7 +105,7 @@ def predict(self, X): return sparse.csr_matrix(pred > 0.2) def predict_proba(self, X): - pred = self.model.predict_generator(generator=_batch_generatorp(X, self.batch_size), val_samples=X.shape[0]) + pred = self.model.predict_generator(generator=_batch_generatorp(X, self.batch_size), steps=int(X.shape[0] / float(self.batch_size)) + 1) return pred From cd1a005aa2fddcdc8482bd153956cd4aa36582d7 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 18 Jun 2017 10:37:01 +0200 Subject: [PATCH 011/116] Learning rate and number of epochs for MLP as command line parameter --- Code/lucid_ml/classifying/neural_net.py | 10 +++++++--- Code/lucid_ml/run.py | 10 ++++++++-- 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/neural_net.py b/Code/lucid_ml/classifying/neural_net.py index c310a72..b56dac9 100644 --- a/Code/lucid_ml/classifying/neural_net.py +++ b/Code/lucid_ml/classifying/neural_net.py @@ -64,18 +64,22 @@ def _batch_generatorp(X, batch_size): class MLP(BaseEstimator): - def __init__(self, verbose=0, model=None, final_activation='sigmoid', batch_size = 512): + def __init__(self, verbose=0, model=None, final_activation='sigmoid', batch_size = 512, learning_rate = None, epochs = 20): self.verbose = verbose self.model = model self.final_activation = final_activation self.batch_size = batch_size self.validation_data_position = None + self.epochs = epochs # we scale the learning rate proportionally with the batch size as suggested by # [Thomas M. Breuel, 2015, The Effects of Hyperparameters on SGD # Training of Neural Networks] # we found lr=0.01 to be a good learning rate for batch size 512 - self.lr = self.batch_size / 512 * 0.01 + if learning_rate is None: + self.lr = self.batch_size / 512 * 0.01 + else: + self.lr = learning_rate def fit(self, X, y): if not self.model: @@ -96,7 +100,7 @@ def fit(self, X, y): else: X_train, y_train = X, y self.model.fit_generator(generator=_batch_generator(X_train, y_train, self.batch_size, True), callbacks=callbacks, - steps_per_epoch=int(X.shape[0] / float(self.batch_size)) + 1, nb_epoch=20, verbose=self.verbose, + steps_per_epoch=int(X.shape[0] / float(self.batch_size)) + 1, nb_epoch=self.epochs, verbose=self.verbose, validation_data = _batch_generator(X_val, y_val, self.batch_size, False) if self.validation_data_position is not None else None, validation_steps = 10) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 5ca7492..220d513 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -353,7 +353,7 @@ def create_classifier(options, num_concepts): l2r_metric = options.l2r_metric + "@20", n_jobs = options.jobs, translation_probability = options.translation_prob) - mlp = MLP(verbose=options.verbose, batch_size = options.batch_size) + mlp = MLP(verbose=options.verbose, batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations) classifiers = { "nn": NearestNeighbor(use_lsh_forest=options.lshf), "brknna": BRKNeighborsClassifier(mode='a', n_neighbors=options.k, use_lsh_forest=options.lshf, @@ -375,7 +375,10 @@ def create_classifier(options, num_concepts): "rocchiodt": ClassifierStack(base_classifier=RocchioClassifier(metric = 'cosine'), n_jobs=options.jobs, n=options.k), "logregressdt": ClassifierStack(base_classifier=logregress, n_jobs=options.jobs, n=options.k), "mlp": mlp, - "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size), alpha=options.alpha, stepsize=0.01, verbose=options.verbose), + "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, + learning_rate = options.learning_rate, + epochs = options.max_iterations), + alpha=options.alpha, stepsize=0.01, verbose=options.verbose), "mlpthr": LinRegStack(mlp, verbose=options.verbose), "mlpdt" : ClassifierStack(base_classifier=mlp, n_jobs=options.jobs, n=options.k) } @@ -513,6 +516,9 @@ def _generate_parsers(): "Performs Grid search to find optimal K") classifier_options.add_argument('-e', type=int, dest="max_iterations", default=5, help= "Determine the number of epochs for the training of several classifiers [5]") + classifier_options.add_argument('--learning_rate', type=float, dest="learning_rate", default=None, help= + "Determine the learning rate for training of several classifiers. If set to 'None', the learning rate is automatically based on an empirical good value and \ + adapted to the batch size. [None]") classifier_options.add_argument('-P', type=str, dest="penalty", default=None, choices=['l1','l2','elasticnet'], help=\ "Penalty term for SGD and other regularized linear models") classifier_options.add_argument('--l2r-alg', type=str, dest="l2r", default="listnet", choices=['listnet','adarank','ca', 'lambdamart'], help=\ From 17dec4b270ed0afa46b32b421e34b2188d0e1050 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 18 Jun 2017 11:10:45 +0200 Subject: [PATCH 012/116] Use model checkpoint of best model after early stopping --- Code/lucid_ml/classifying/neural_net.py | 13 ++++++++----- Code/requirements.txt | 1 + 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/neural_net.py b/Code/lucid_ml/classifying/neural_net.py index b56dac9..cec3c0c 100644 --- a/Code/lucid_ml/classifying/neural_net.py +++ b/Code/lucid_ml/classifying/neural_net.py @@ -3,15 +3,13 @@ from scipy import sparse from sklearn.base import BaseEstimator -from keras.layers import Dense, Activation, Dropout, BatchNormalization +from keras.layers import Dense, Activation, Dropout from keras.models import Sequential from keras.optimizers import Adam import numpy as np from sklearn.metrics import f1_score from sklearn.linear_model import Ridge -from keras.callbacks import EarlyStopping -from keras.callbacks import Callback -from sklearn.metrics import f1_score +from keras.callbacks import EarlyStopping, ModelCheckpoint #=============================================================================== # class EarlyStoppingBySklearnMetric(Callback): @@ -93,9 +91,11 @@ def fit(self, X, y): val_pos = self.validation_data_position + callbacks = [] if self.validation_data_position is not None: - callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')) + callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')) + callbacks.append(ModelCheckpoint("weights.best.hdf5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')) X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] else: X_train, y_train = X, y @@ -103,6 +103,9 @@ def fit(self, X, y): steps_per_epoch=int(X.shape[0] / float(self.batch_size)) + 1, nb_epoch=self.epochs, verbose=self.verbose, validation_data = _batch_generator(X_val, y_val, self.batch_size, False) if self.validation_data_position is not None else None, validation_steps = 10) + + if self.validation_data_position is not None: + self.model.load_weights("weights.best.hdf5") def predict(self, X): pred = self.predict_proba(X) diff --git a/Code/requirements.txt b/Code/requirements.txt index af65f9a..f4addec 100644 --- a/Code/requirements.txt +++ b/Code/requirements.txt @@ -10,3 +10,4 @@ decorator pandas tensorflow keras +h5py From 749e7c021bed8b90e18b81a1222b4b55eff3cca8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 19 Jun 2017 12:40:11 +0200 Subject: [PATCH 013/116] First version of tensorflow reimplementation of MLP --- Code/lucid_ml/classifying/soph_mlp.py | 137 ++++++++++++++++++++++++++ Code/lucid_ml/run.py | 8 +- 2 files changed, 143 insertions(+), 2 deletions(-) create mode 100644 Code/lucid_ml/classifying/soph_mlp.py diff --git a/Code/lucid_ml/classifying/soph_mlp.py b/Code/lucid_ml/classifying/soph_mlp.py new file mode 100644 index 0000000..9ab05f1 --- /dev/null +++ b/Code/lucid_ml/classifying/soph_mlp.py @@ -0,0 +1,137 @@ +import numpy as np +import tensorflow as tf +from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib +from scipy.sparse.csr import csr_matrix +import scipy.sparse as sps +from pandas.core.sparse.array import _sparray_doc_kwargs +tf.logging.set_verbosity(tf.logging.INFO) + +def mlp_soph_fn(features, targets, mode, params): + """Model function for MLP-Soph.""" + + targets = tf.to_float(targets) + print("Creating the model") + # Connect the first hidden layer to input layer + # (features) with relu activation + features = features["bow"] + first_hidden_layer = tf.contrib.layers.relu(features, 10) + + # Connect the second hidden layer to first hidden layer with relu + second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) + + # Connect the output layer to second hidden layer (no activation fn) + + num_classes = targets.get_shape()[1].value + output_layer = tf.contrib.layers.linear(second_hidden_layer, + num_outputs=num_classes) + + # cast to float32 + output_layer = tf.to_float(output_layer) + + # Reshape output layer to 1-dim Tensor to return predictions + #predictions = tf.reshape(output_layer, [-1]) + predictions = tf.contrib.layers.fully_connected(inputs=second_hidden_layer, + num_outputs=num_classes, + activation_fn=tf.sigmoid) + predictions_dict = {"labels": predictions} + + # Calculate loss using mean squared error + losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) + loss = tf.reduce_sum(losses) + + # Calculate root mean squared error as additional eval metric + #=========================================================================== + # eval_metric_ops = { + # "rmse": + # tf.metrics.root_mean_squared_error( + # tf.cast(targets, tf.float64), predictions) + # } + #=========================================================================== + + train_op = tf.contrib.layers.optimize_loss( + loss=loss, + global_step=tf.contrib.framework.get_global_step(), + learning_rate=params["learning_rate"], + optimizer="SGD") + print("Done creating the model I guess") + return model_fn_lib.ModelFnOps( + mode=mode, + predictions=predictions_dict, + loss=loss, + train_op=train_op) + +def _batch_generator(X, y, batch_size, shuffle): + number_of_batches = np.ceil(X.shape[0] / batch_size) + counter = 0 + sample_index = np.arange(X.shape[0]) + if shuffle: + np.random.shuffle(sample_index) + while True: + batch_index = sample_index[batch_size * counter:batch_size * (counter + 1)] + X_batch = X[batch_index, :].toarray() + y_batch = y[batch_index].toarray() + counter += 1 + yield tf.constant(X_batch), tf.constant(y_batch) + if counter == number_of_batches: + if shuffle: + np.random.shuffle(sample_index) + counter = 0 + +class MLP_Soph(): + + def _sparse_to_sparse(self, X): + non_zero_rows, non_zero_cols = X.nonzero() + indices = np.array([[r, c] for r, c in zip(non_zero_rows, non_zero_cols)]) + values = np.array([X[r,c] for r, c in zip(non_zero_rows, non_zero_cols)]) + + shape = list(self.X.shape) + input_tensor = tf.SparseTensor(indices=indices, + values=values, + dense_shape=shape) + return input_tensor + + def _my_input_fn_fit(self, X, y, predict = False): + if sps.issparse(X): + #input_tensor = self._sparse_to_sparse(self.X) + input_tensor = tf.constant(X.todense()) + else: + input_tensor = tf.constant(X) + + if not predict: + if sps.issparse(self.y): + #label_tensor = self._sparse_to_sparse(self.y) + label_tensor = tf.constant(y.todense()) + else: + label_tensor = tf.constant(y) + + + feature_cols = {"bow" : input_tensor} + if not predict: + return feature_cols, label_tensor + else: + return feature_cols + + def __init__(self): + + model_params = {"learning_rate": 0.1} + + model_fn = mlp_soph_fn + self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params) + + + def fit(self, X, y): + + self.X = X + self.y = y + self._estimator.fit(input_fn=lambda : self._my_input_fn_fit(X, y, predict=False), steps=2000) + + + def predict(self, X): + prediction = self._estimator.predict(input_fn=lambda : self._my_input_fn_fit(X, None, predict=True), as_iterable = False)["labels"] + + threshold = 0.2 + prediction[prediction > threshold] = 1 + prediction[prediction <= threshold] = 0 + return csr_matrix(prediction) + + \ No newline at end of file diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 220d513..e9f5e7a 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -31,12 +31,15 @@ from sklearn.preprocessing import MultiLabelBinarizer from sklearn.svm import LinearSVC +import tensorflow as tf + from classifying.br_kneighbor_classifier import BRKNeighborsClassifier from classifying.kneighbour_l2r_classifier import KNeighborsL2RClassifier from classifying.meancut_kneighbor_classifier import MeanCutKNeighborsClassifier from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack +from classifying.soph_mlp import MLP_Soph from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -256,7 +259,7 @@ def split(self, X): Y_train = np.vstack((Y_train, Y_val)) # mlp doesn't seem to like being stuck into a new process... - if options.debug or options.clf_key in {'mlp', 'mlpthr'}: + if options.debug or options.clf_key in {'mlp', 'mlpthr', 'mlpsoph'}: Y_pred, Y_train_pred = fit_predict(X_test, X_train, Y_train, options, tr, clf) else: Y_pred, Y_train_pred = fit_predict_new_process(X_test, X_train, Y_train, options, tr, clf) @@ -375,6 +378,7 @@ def create_classifier(options, num_concepts): "rocchiodt": ClassifierStack(base_classifier=RocchioClassifier(metric = 'cosine'), n_jobs=options.jobs, n=options.k), "logregressdt": ClassifierStack(base_classifier=logregress, n_jobs=options.jobs, n=options.k), "mlp": mlp, + "mlpsoph" : MLP_Soph(), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -502,7 +506,7 @@ def _generate_parsers(): classifier_options.add_argument('-f', '--classifier', dest="clf_key", default="nn", help= "Specify the final classifier.", choices=["nn", "brknna", "brknnb", "bbayes", "mbayes", "lsvc", "sgd", "sgddt", "rocchio", "rocchiodt", "logregress", "logregressdt", - "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam']) + "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpsoph']) classifier_options.add_argument('-a', '--alpha', dest="alpha", type=float, default=1e-7, help= \ "Specify alpha parameter for stochastic gradient descent") classifier_options.add_argument('-n', dest="k", type=int, default=1, help= From 768b70446a5da81c06554b06bceda45fe9c273b8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 19 Jun 2017 14:39:01 +0200 Subject: [PATCH 014/116] Wrapper class for tensorflow models in general with mini batching --- Code/lucid_ml/classifying/soph_mlp.py | 137 ---------------- .../lucid_ml/classifying/tensorflow_models.py | 147 ++++++++++++++++++ Code/lucid_ml/run.py | 4 +- 3 files changed, 149 insertions(+), 139 deletions(-) delete mode 100644 Code/lucid_ml/classifying/soph_mlp.py create mode 100644 Code/lucid_ml/classifying/tensorflow_models.py diff --git a/Code/lucid_ml/classifying/soph_mlp.py b/Code/lucid_ml/classifying/soph_mlp.py deleted file mode 100644 index 9ab05f1..0000000 --- a/Code/lucid_ml/classifying/soph_mlp.py +++ /dev/null @@ -1,137 +0,0 @@ -import numpy as np -import tensorflow as tf -from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib -from scipy.sparse.csr import csr_matrix -import scipy.sparse as sps -from pandas.core.sparse.array import _sparray_doc_kwargs -tf.logging.set_verbosity(tf.logging.INFO) - -def mlp_soph_fn(features, targets, mode, params): - """Model function for MLP-Soph.""" - - targets = tf.to_float(targets) - print("Creating the model") - # Connect the first hidden layer to input layer - # (features) with relu activation - features = features["bow"] - first_hidden_layer = tf.contrib.layers.relu(features, 10) - - # Connect the second hidden layer to first hidden layer with relu - second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) - - # Connect the output layer to second hidden layer (no activation fn) - - num_classes = targets.get_shape()[1].value - output_layer = tf.contrib.layers.linear(second_hidden_layer, - num_outputs=num_classes) - - # cast to float32 - output_layer = tf.to_float(output_layer) - - # Reshape output layer to 1-dim Tensor to return predictions - #predictions = tf.reshape(output_layer, [-1]) - predictions = tf.contrib.layers.fully_connected(inputs=second_hidden_layer, - num_outputs=num_classes, - activation_fn=tf.sigmoid) - predictions_dict = {"labels": predictions} - - # Calculate loss using mean squared error - losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) - loss = tf.reduce_sum(losses) - - # Calculate root mean squared error as additional eval metric - #=========================================================================== - # eval_metric_ops = { - # "rmse": - # tf.metrics.root_mean_squared_error( - # tf.cast(targets, tf.float64), predictions) - # } - #=========================================================================== - - train_op = tf.contrib.layers.optimize_loss( - loss=loss, - global_step=tf.contrib.framework.get_global_step(), - learning_rate=params["learning_rate"], - optimizer="SGD") - print("Done creating the model I guess") - return model_fn_lib.ModelFnOps( - mode=mode, - predictions=predictions_dict, - loss=loss, - train_op=train_op) - -def _batch_generator(X, y, batch_size, shuffle): - number_of_batches = np.ceil(X.shape[0] / batch_size) - counter = 0 - sample_index = np.arange(X.shape[0]) - if shuffle: - np.random.shuffle(sample_index) - while True: - batch_index = sample_index[batch_size * counter:batch_size * (counter + 1)] - X_batch = X[batch_index, :].toarray() - y_batch = y[batch_index].toarray() - counter += 1 - yield tf.constant(X_batch), tf.constant(y_batch) - if counter == number_of_batches: - if shuffle: - np.random.shuffle(sample_index) - counter = 0 - -class MLP_Soph(): - - def _sparse_to_sparse(self, X): - non_zero_rows, non_zero_cols = X.nonzero() - indices = np.array([[r, c] for r, c in zip(non_zero_rows, non_zero_cols)]) - values = np.array([X[r,c] for r, c in zip(non_zero_rows, non_zero_cols)]) - - shape = list(self.X.shape) - input_tensor = tf.SparseTensor(indices=indices, - values=values, - dense_shape=shape) - return input_tensor - - def _my_input_fn_fit(self, X, y, predict = False): - if sps.issparse(X): - #input_tensor = self._sparse_to_sparse(self.X) - input_tensor = tf.constant(X.todense()) - else: - input_tensor = tf.constant(X) - - if not predict: - if sps.issparse(self.y): - #label_tensor = self._sparse_to_sparse(self.y) - label_tensor = tf.constant(y.todense()) - else: - label_tensor = tf.constant(y) - - - feature_cols = {"bow" : input_tensor} - if not predict: - return feature_cols, label_tensor - else: - return feature_cols - - def __init__(self): - - model_params = {"learning_rate": 0.1} - - model_fn = mlp_soph_fn - self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params) - - - def fit(self, X, y): - - self.X = X - self.y = y - self._estimator.fit(input_fn=lambda : self._my_input_fn_fit(X, y, predict=False), steps=2000) - - - def predict(self, X): - prediction = self._estimator.predict(input_fn=lambda : self._my_input_fn_fit(X, None, predict=True), as_iterable = False)["labels"] - - threshold = 0.2 - prediction[prediction > threshold] = 1 - prediction[prediction <= threshold] = 0 - return csr_matrix(prediction) - - \ No newline at end of file diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py new file mode 100644 index 0000000..91e5eeb --- /dev/null +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -0,0 +1,147 @@ +import numpy as np +import tensorflow as tf +from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib +from scipy.sparse.csr import csr_matrix +from sklearn.base import BaseEstimator +tf.logging.set_verbosity(tf.logging.INFO) + +def mlp_soph_fn(features, targets, mode, params): + """Model function for MLP-Soph.""" + + targets = tf.to_float(targets) + print("Creating the model") + # Connect the first hidden layer to input layer + # (features) with relu activation + first_hidden_layer = tf.contrib.layers.relu(features, 10) + + # Connect the second hidden layer to first hidden layer with relu + second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) + + # Connect the output layer to second hidden layer (no activation fn) + + num_classes = targets.get_shape()[1].value + output_layer = tf.contrib.layers.linear(second_hidden_layer, + num_outputs=num_classes) + + # cast to float32 + output_layer = tf.to_float(output_layer) + + # Reshape output layer to 1-dim Tensor to return predictions + #predictions = tf.reshape(output_layer, [-1]) + predictions = tf.contrib.layers.fully_connected(inputs=second_hidden_layer, + num_outputs=num_classes, + activation_fn=tf.sigmoid) + + # Calculate loss using mean squared error + losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) + loss = tf.reduce_sum(losses) + + # Calculate root mean squared error as additional eval metric + #=========================================================================== + # eval_metric_ops = { + # "rmse": + # tf.metrics.root_mean_squared_error( + # tf.cast(targets, tf.float64), predictions) + # } + #=========================================================================== + + train_op = tf.contrib.layers.optimize_loss( + loss=loss, + global_step=tf.contrib.framework.get_global_step(), + learning_rate=params["learning_rate"], + optimizer="SGD") + print("Done creating the model I guess") + return model_fn_lib.ModelFnOps( + mode=mode, + predictions=predictions, + loss=loss, + train_op=train_op) + +class BatchGenerator: + + def __init__(self, X, y, batch_size, shuffle, predict): + self.X = X + self.y = y + self.number_of_batches = np.ceil(X.shape[0] / batch_size) + self.counter = 0 + self.sample_index = np.arange(X.shape[0]) + self.batch_size = batch_size + self.predict = predict + if shuffle: + np.random.shuffle(self.sample_index) + + + def _batch_generator(self): + + batch_index = self.sample_index[self.batch_size * self.counter:self.batch_size * (self.counter + 1)] + X_batch = self.X[batch_index, :].toarray() + if not self.predict: + y_batch = self.y[batch_index].toarray() + self.counter += 1 + if self.counter == self.number_of_batches: + if self.shuffle: + np.random.shuffle(self.sample_index) + self.counter = 0 + if not self.predict: + return tf.constant(X_batch), tf.constant(y_batch) + else: + return tf.constant(X_batch) + +def mlp_soph(): + return mlp_soph_fn, {"learning_rate": 0.01} + +class MultiLabelSKFlow(BaseEstimator): + """ + This is a wrapper class for tf.contrib.learn.Estimators, so it adheres to the fit/predict naming conventions of sk-learn. + It already handles mini-batching, whose behavior can be controlled by providing the respective parameters to the init function. + + The concrete TensorFlow model to execute can be specified in terms of the 'get_model' function. + This function in turn has to return a 'model_fn' function and a 'params' dictionary. + These arguments will passed to the tf.contrib.learn.Estimator class and have thus to conform the formats + described in 'https://www.tensorflow.org/extend/estimators'. + On top of that, 'model_fn' has to assume the 'features' and 'targets' parameters to be of the Tensor class. + """ + + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph): + """ + + """ + + # used by this class + self.batch_size = batch_size + self.num_epochs = num_epochs + + # used by the tensorflow model function + model_fn, model_params = get_model() + + self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params) + + + def fit(self, X, y): + + self.X = X + self.y = y + batch_generator = BatchGenerator(X, y, self.batch_size, True, False) + steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) + self._estimator.fit(input_fn=lambda : batch_generator._batch_generator(), steps=self.num_epochs * steps_per_epoch) + + + def predict(self, X): + + # TODO: implement batch prediction + prediction_steps = int(np.ceil(X.shape[0] / self.batch_size)) + + prediction = np.zeros((X.shape[0], self.y.shape[1])) + for i in range(prediction_steps): + batch_generator = BatchGenerator(X, None, self.batch_size, False, True) + pred_generator = self._estimator.predict(input_fn=lambda : batch_generator._batch_generator()) + pred_batch = np.array([single_prediction for single_prediction in pred_generator]) + print(pred_batch.shape) + prediction[i * self.batch_size:(i+1) * self.batch_size] = pred_batch + + threshold = 0.2 + prediction[prediction > threshold] = 1 + prediction[prediction <= threshold] = 0 + return csr_matrix(prediction) + + \ No newline at end of file diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index e9f5e7a..057ecce 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -39,7 +39,7 @@ from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack -from classifying.soph_mlp import MLP_Soph +from classifying.tensorflow_models import MultiLabelSKFlow from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -378,7 +378,7 @@ def create_classifier(options, num_concepts): "rocchiodt": ClassifierStack(base_classifier=RocchioClassifier(metric = 'cosine'), n_jobs=options.jobs, n=options.k), "logregressdt": ClassifierStack(base_classifier=logregress, n_jobs=options.jobs, n=options.k), "mlp": mlp, - "mlpsoph" : MLP_Soph(), + "mlpsoph" : MultiLabelSKFlow(), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), From f4e9c678ad099131e2b016eaf4363be8a1e58a42 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 19 Jun 2017 16:19:44 +0200 Subject: [PATCH 015/116] MLP_Soph now replicates MLP_Base in tensorflow instead of keras --- .../lucid_ml/classifying/tensorflow_models.py | 56 +++++++++++++------ Code/lucid_ml/run.py | 11 +++- 2 files changed, 49 insertions(+), 18 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 91e5eeb..32abfb9 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -3,24 +3,23 @@ from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib from scipy.sparse.csr import csr_matrix from sklearn.base import BaseEstimator + tf.logging.set_verbosity(tf.logging.INFO) def mlp_soph_fn(features, targets, mode, params): """Model function for MLP-Soph.""" targets = tf.to_float(targets) - print("Creating the model") # Connect the first hidden layer to input layer # (features) with relu activation - first_hidden_layer = tf.contrib.layers.relu(features, 10) + hidden_layer = tf.contrib.layers.relu(features, 1000) - # Connect the second hidden layer to first hidden layer with relu - second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 10) + hidden_dropout = tf.nn.dropout(hidden_layer, params["dropout"]) # Connect the output layer to second hidden layer (no activation fn) num_classes = targets.get_shape()[1].value - output_layer = tf.contrib.layers.linear(second_hidden_layer, + output_layer = tf.contrib.layers.linear(hidden_dropout, num_outputs=num_classes) # cast to float32 @@ -28,7 +27,7 @@ def mlp_soph_fn(features, targets, mode, params): # Reshape output layer to 1-dim Tensor to return predictions #predictions = tf.reshape(output_layer, [-1]) - predictions = tf.contrib.layers.fully_connected(inputs=second_hidden_layer, + predictions = tf.contrib.layers.fully_connected(inputs=hidden_dropout, num_outputs=num_classes, activation_fn=tf.sigmoid) @@ -49,8 +48,7 @@ def mlp_soph_fn(features, targets, mode, params): loss=loss, global_step=tf.contrib.framework.get_global_step(), learning_rate=params["learning_rate"], - optimizer="SGD") - print("Done creating the model I guess") + optimizer="Adam") return model_fn_lib.ModelFnOps( mode=mode, predictions=predictions, @@ -67,6 +65,7 @@ def __init__(self, X, y, batch_size, shuffle, predict): self.sample_index = np.arange(X.shape[0]) self.batch_size = batch_size self.predict = predict + self.shuffle = shuffle if shuffle: np.random.shuffle(self.sample_index) @@ -87,8 +86,12 @@ def _batch_generator(self): else: return tf.constant(X_batch) -def mlp_soph(): - return mlp_soph_fn, {"learning_rate": 0.01} + +def mlp_soph(lr, dropout): + if lr is None: + # set lr to default option + lr = 0.1 + return lambda : (mlp_soph_fn, {"learning_rate": lr, "dropout" : dropout}) class MultiLabelSKFlow(BaseEstimator): """ @@ -102,28 +105,49 @@ class MultiLabelSKFlow(BaseEstimator): On top of that, 'model_fn' has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.1, 0.5)): """ """ + # enable early stopping on validation set + self.validation_data_position = None + # used by this class self.batch_size = batch_size self.num_epochs = num_epochs # used by the tensorflow model function model_fn, model_params = get_model() - self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params) def fit(self, X, y): - self.X = X self.y = y - batch_generator = BatchGenerator(X, y, self.batch_size, True, False) - steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) - self._estimator.fit(input_fn=lambda : batch_generator._batch_generator(), steps=self.num_epochs * steps_per_epoch) + + val_pos = self.validation_data_position + monitors = [] + if val_pos is not None: + #callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')) + #callbacks.append(ModelCheckpoint("weights.best.hdf5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')) + X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] + + validation_batch_generator = BatchGenerator(X_val, y_val, X_val.shape[0], False, False) + steps_per_epoch = int(np.ceil(X_train.shape[0] / self.batch_size)) + monitors.append(tf.contrib.learn.monitors.ValidationMonitor(input_fn = validation_batch_generator._batch_generator, + every_n_steps=steps_per_epoch, + early_stopping_metric="loss", + early_stopping_rounds=5 * steps_per_epoch, + eval_steps = int(np.ceil(X_val.shape[0] / self.batch_size)))) + else: + steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) + X_train = X + y_train = y + + batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) + + self._estimator.fit(input_fn=lambda : batch_generator._batch_generator(), steps=self.num_epochs * steps_per_epoch, monitors = monitors) def predict(self, X): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 057ecce..6f4a37f 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -39,7 +39,7 @@ from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack -from classifying.tensorflow_models import MultiLabelSKFlow +from classifying.tensorflow_models import MultiLabelSKFlow, mlp_soph from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -378,7 +378,9 @@ def create_classifier(options, num_concepts): "rocchiodt": ClassifierStack(base_classifier=RocchioClassifier(metric = 'cosine'), n_jobs=options.jobs, n=options.k), "logregressdt": ClassifierStack(base_classifier=logregress, n_jobs=options.jobs, n=options.k), "mlp": mlp, - "mlpsoph" : MultiLabelSKFlow(), + "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, + num_epochs=options.max_iterations, + get_model = mlp_soph(options.learning_rate, options.dropout)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -537,6 +539,11 @@ def _generate_parsers(): "Specify n_neighbors argument for KneighborsL2RClassifier.") classifier_options.add_argument('--batch_size', dest="batch_size", type=int, default=256, help= "Specify batch size for neural network training.") + + # neural network specific options + neural_network_options = parser.add_argument_group("Neural Network Options") + neural_network_options.add_argument('--dropout', type=float, dest="dropout", default=0.5, help= + "Determine the keep probability for all dropout layers.", choices=range(0,1)) # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 14ca61a2cb24c5983e2a76906992a3e7b11e0880 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 20 Jun 2017 11:30:16 +0200 Subject: [PATCH 016/116] Pass sparse tensors directly to tf-models to circumvent tf.constant size limitations --- .../lucid_ml/classifying/tensorflow_models.py | 38 +++++++++++++++---- 1 file changed, 30 insertions(+), 8 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 32abfb9..9c157a2 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -8,12 +8,22 @@ def mlp_soph_fn(features, targets, mode, params): """Model function for MLP-Soph.""" + # convert sparse tensors to dense + num_samples = features.get_shape()[0].value + num_cols = features.get_shape()[1].value + features = tf.sparse_reorder(features) + features = tf.sparse_tensor_to_dense(features) + features = tf.reshape(features, [num_samples, num_cols]) + num_classes = targets.get_shape()[1].value + targets = tf.sparse_reorder(targets) + targets = tf.sparse_tensor_to_dense(targets) targets = tf.to_float(targets) + targets = tf.reshape(targets, [num_samples, num_classes]) + # Connect the first hidden layer to input layer - # (features) with relu activation + # (features) with relu activation and add dropout hidden_layer = tf.contrib.layers.relu(features, 1000) - hidden_dropout = tf.nn.dropout(hidden_layer, params["dropout"]) # Connect the output layer to second hidden layer (no activation fn) @@ -73,18 +83,18 @@ def __init__(self, X, y, batch_size, shuffle, predict): def _batch_generator(self): batch_index = self.sample_index[self.batch_size * self.counter:self.batch_size * (self.counter + 1)] - X_batch = self.X[batch_index, :].toarray() + X_batch = self.X[batch_index, :] if not self.predict: - y_batch = self.y[batch_index].toarray() + y_batch = self.y[batch_index] self.counter += 1 if self.counter == self.number_of_batches: if self.shuffle: np.random.shuffle(self.sample_index) self.counter = 0 if not self.predict: - return tf.constant(X_batch), tf.constant(y_batch) + return _sparse_to_sparse(X_batch), _sparse_to_sparse(y_batch) else: - return tf.constant(X_batch) + return _sparse_to_sparse(X_batch) def mlp_soph(lr, dropout): @@ -93,6 +103,19 @@ def mlp_soph(lr, dropout): lr = 0.1 return lambda : (mlp_soph_fn, {"learning_rate": lr, "dropout" : dropout}) +def _sparse_to_sparse(X): + non_zero_rows, non_zero_cols = X.nonzero() + indices = np.array([[r, c] for r, c in zip(non_zero_rows, non_zero_cols)]) + values = np.array([X[r,c] for r, c in zip(non_zero_rows, non_zero_cols)]) + + shape = list(X.shape) + input_tensor = tf.SparseTensor(indices=indices, + values=values, + dense_shape=shape) + return input_tensor + + + class MultiLabelSKFlow(BaseEstimator): """ This is a wrapper class for tf.contrib.learn.Estimators, so it adheres to the fit/predict naming conventions of sk-learn. @@ -160,7 +183,6 @@ def predict(self, X): batch_generator = BatchGenerator(X, None, self.batch_size, False, True) pred_generator = self._estimator.predict(input_fn=lambda : batch_generator._batch_generator()) pred_batch = np.array([single_prediction for single_prediction in pred_generator]) - print(pred_batch.shape) prediction[i * self.batch_size:(i+1) * self.batch_size] = pred_batch threshold = 0.2 @@ -168,4 +190,4 @@ def predict(self, X): prediction[prediction <= threshold] = 0 return csr_matrix(prediction) - \ No newline at end of file + From 52f4779e3d67610e44ced3f40df1b024eb6b53f0 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 20 Jun 2017 13:02:09 +0200 Subject: [PATCH 017/116] Fixed prediction step for sophmlp --- .../lucid_ml/classifying/tensorflow_models.py | 52 ++++++++++++------- 1 file changed, 32 insertions(+), 20 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 9c157a2..b2a541b 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -6,20 +6,21 @@ tf.logging.set_verbosity(tf.logging.INFO) -def mlp_soph_fn(features, targets, mode, params): +def mlp_soph_fn(features, targets, mode, params, num_classes): """Model function for MLP-Soph.""" # convert sparse tensors to dense + num_samples = features.get_shape()[0].value num_cols = features.get_shape()[1].value features = tf.sparse_reorder(features) features = tf.sparse_tensor_to_dense(features) features = tf.reshape(features, [num_samples, num_cols]) - num_classes = targets.get_shape()[1].value - targets = tf.sparse_reorder(targets) - targets = tf.sparse_tensor_to_dense(targets) - targets = tf.to_float(targets) - targets = tf.reshape(targets, [num_samples, num_classes]) + if mode != tf.contrib.learn.ModeKeys.INFER: + targets = tf.sparse_reorder(targets) + targets = tf.sparse_tensor_to_dense(targets) + targets = tf.to_float(targets) + targets = tf.reshape(targets, [num_samples, num_classes]) # Connect the first hidden layer to input layer # (features) with relu activation and add dropout @@ -28,7 +29,6 @@ def mlp_soph_fn(features, targets, mode, params): # Connect the output layer to second hidden layer (no activation fn) - num_classes = targets.get_shape()[1].value output_layer = tf.contrib.layers.linear(hidden_dropout, num_outputs=num_classes) @@ -42,8 +42,11 @@ def mlp_soph_fn(features, targets, mode, params): activation_fn=tf.sigmoid) # Calculate loss using mean squared error - losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) - loss = tf.reduce_sum(losses) + if mode != tf.contrib.learn.ModeKeys.INFER: + losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) + loss = tf.reduce_sum(losses) + else: + loss = tf.constant(0.) # Calculate root mean squared error as additional eval metric #=========================================================================== @@ -54,11 +57,14 @@ def mlp_soph_fn(features, targets, mode, params): # } #=========================================================================== - train_op = tf.contrib.layers.optimize_loss( - loss=loss, - global_step=tf.contrib.framework.get_global_step(), - learning_rate=params["learning_rate"], - optimizer="Adam") + if mode != tf.contrib.learn.ModeKeys.INFER: + train_op = tf.contrib.layers.optimize_loss( + loss=loss, + global_step=tf.contrib.framework.get_global_step(), + learning_rate=params["learning_rate"], + optimizer="Adam") + else: + train_op = None return model_fn_lib.ModelFnOps( mode=mode, predictions=predictions, @@ -123,9 +129,10 @@ class MultiLabelSKFlow(BaseEstimator): The concrete TensorFlow model to execute can be specified in terms of the 'get_model' function. This function in turn has to return a 'model_fn' function and a 'params' dictionary. - These arguments will passed to the tf.contrib.learn.Estimator class and have thus to conform the formats + These arguments will passed to the tf.contrib.learn. Estimator class and have thus to conform the formats described in 'https://www.tensorflow.org/extend/estimators'. - On top of that, 'model_fn' has to assume the 'features' and 'targets' parameters to be of the Tensor class. + On top of that, 'model_fn' has to accept an additional, non-positional argument 'num_classes' which is used to + infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.1, 0.5)): @@ -141,14 +148,19 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.1, 0. self.num_epochs = num_epochs # used by the tensorflow model function - model_fn, model_params = get_model() - self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params) + print(get_model()) + self.model_fn, self.model_params = get_model() def fit(self, X, y): self.y = y + def model_fn_with_num_classes(features, targets, mode, params): + return self.model_fn(features, targets, mode, params, y.shape[1]) + + self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn_with_num_classes, params=self.model_params) + val_pos = self.validation_data_position monitors = [] if val_pos is not None: @@ -170,7 +182,7 @@ def fit(self, X, y): batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) - self._estimator.fit(input_fn=lambda : batch_generator._batch_generator(), steps=self.num_epochs * steps_per_epoch, monitors = monitors) + self._estimator.fit(input_fn= batch_generator._batch_generator, steps=self.num_epochs * steps_per_epoch, monitors = monitors) def predict(self, X): @@ -181,7 +193,7 @@ def predict(self, X): prediction = np.zeros((X.shape[0], self.y.shape[1])) for i in range(prediction_steps): batch_generator = BatchGenerator(X, None, self.batch_size, False, True) - pred_generator = self._estimator.predict(input_fn=lambda : batch_generator._batch_generator()) + pred_generator = self._estimator.predict(input_fn=batch_generator._batch_generator) pred_batch = np.array([single_prediction for single_prediction in pred_generator]) prediction[i * self.batch_size:(i+1) * self.batch_size] = pred_batch From 0d3035d84458f435edfae556e3800b154a86d154 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 20 Jun 2017 15:58:50 +0200 Subject: [PATCH 018/116] Fixed dropout parameter --- Code/lucid_ml/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 6f4a37f..cb33080 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -543,7 +543,7 @@ def _generate_parsers(): # neural network specific options neural_network_options = parser.add_argument_group("Neural Network Options") neural_network_options.add_argument('--dropout', type=float, dest="dropout", default=0.5, help= - "Determine the keep probability for all dropout layers.", choices=range(0,1)) + "Determine the keep probability for all dropout layers.") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 007ec5fa4a07d7cdab25f12bc773ec2e496e9ec4 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 14:49:43 +0200 Subject: [PATCH 019/116] Switched from tf.contrib.learn to plain tensorflow due to issues with outdated documentation and deprecated functionality --- .../lucid_ml/classifying/tensorflow_models.py | 308 ++++++++++++------ Code/lucid_ml/run.py | 3 +- 2 files changed, 206 insertions(+), 105 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index b2a541b..e85a463 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -6,70 +6,80 @@ tf.logging.set_verbosity(tf.logging.INFO) -def mlp_soph_fn(features, targets, mode, params, num_classes): +#=============================================================================== +# def cnn_fn(features, targets, mode, params, num_classes): +# """Model function for CNN.""" +# # convert sparse tensors to dense +# +# EMBEDDING_SIZE = 50 +# +# W = tf.Variable(tf.random_uniform([vocab_size, EMBEDDING_SIZE], -1.0, 1.0), +# name="W") +# self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x) +# self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1) +# +# if mode != tf.contrib.learn.ModeKeys.INFER: +# targets = tf.sparse_reorder(targets) +# targets = tf.sparse_tensor_to_dense(targets) +# targets = tf.to_float(targets) +# targets = tf.reshape(targets, [num_samples, num_classes]) +# +# # Connect the first hidden layer to input layer +# # (features) with relu activation and add dropout +# hidden_layer = tf.contrib.layers.relu(features, 1000) +# hidden_dropout = tf.nn.dropout(hidden_layer, params["dropout"]) +# +# +# # Connect the output layer to second hidden layer (no activation fn) +# +# output_layer = tf.contrib.layers.linear(hidden_dropout, +# num_outputs=num_classes) +# +# output_layer = tf.to_float(output_layer) +# +# # Reshape output layer to 1-dim Tensor to return predictions +# #predictions = tf.reshape(output_layer, [-1]) +# predictions = tf.contrib.layers.fully_connected(inputs=hidden_dropout, +# num_outputs=num_classes, +# activation_fn=tf.sigmoid) +# +# # Calculate loss using mean squared error +# if mode != tf.contrib.learn.ModeKeys.INFER: +# losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) +# loss = tf.reduce_sum(losses) +# else: +# loss = tf.constant(0.) +# +# if mode != tf.contrib.learn.ModeKeys.INFER: +# train_op = tf.contrib.layers.optimize_loss( +# loss=loss, +# global_step=tf.contrib.framework.get_global_step(), +# learning_rate=params["learning_rate"], +# optimizer="Adam") +# else: +# train_op = None +# return model_fn_lib.ModelFnOps( +# mode=mode, +# predictions=predictions, +# loss=loss, +# train_op=train_op) +#=============================================================================== +def mlp_soph_fn(X, y, dropout = 0.5): """Model function for MLP-Soph.""" # convert sparse tensors to dense + x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") + y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") + dropout_tensor = tf.placeholder(tf.float32, name = "dropout") + + params_fit = {dropout_tensor : dropout} + params_predict = {dropout_tensor : 1} - num_samples = features.get_shape()[0].value - num_cols = features.get_shape()[1].value - features = tf.sparse_reorder(features) - features = tf.sparse_tensor_to_dense(features) - features = tf.reshape(features, [num_samples, num_cols]) - - if mode != tf.contrib.learn.ModeKeys.INFER: - targets = tf.sparse_reorder(targets) - targets = tf.sparse_tensor_to_dense(targets) - targets = tf.to_float(targets) - targets = tf.reshape(targets, [num_samples, num_classes]) - # Connect the first hidden layer to input layer # (features) with relu activation and add dropout - hidden_layer = tf.contrib.layers.relu(features, 1000) - hidden_dropout = tf.nn.dropout(hidden_layer, params["dropout"]) - - # Connect the output layer to second hidden layer (no activation fn) - - output_layer = tf.contrib.layers.linear(hidden_dropout, - num_outputs=num_classes) - - # cast to float32 - output_layer = tf.to_float(output_layer) - - # Reshape output layer to 1-dim Tensor to return predictions - #predictions = tf.reshape(output_layer, [-1]) - predictions = tf.contrib.layers.fully_connected(inputs=hidden_dropout, - num_outputs=num_classes, - activation_fn=tf.sigmoid) - - # Calculate loss using mean squared error - if mode != tf.contrib.learn.ModeKeys.INFER: - losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) - loss = tf.reduce_sum(losses) - else: - loss = tf.constant(0.) + hidden_layer = tf.contrib.layers.relu(x_tensor, 1000) + hidden_dropout = tf.nn.dropout(hidden_layer, dropout_tensor) - # Calculate root mean squared error as additional eval metric - #=========================================================================== - # eval_metric_ops = { - # "rmse": - # tf.metrics.root_mean_squared_error( - # tf.cast(targets, tf.float64), predictions) - # } - #=========================================================================== - - if mode != tf.contrib.learn.ModeKeys.INFER: - train_op = tf.contrib.layers.optimize_loss( - loss=loss, - global_step=tf.contrib.framework.get_global_step(), - learning_rate=params["learning_rate"], - optimizer="Adam") - else: - train_op = None - return model_fn_lib.ModelFnOps( - mode=mode, - predictions=predictions, - loss=loss, - train_op=train_op) + return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict class BatchGenerator: @@ -89,36 +99,44 @@ def __init__(self, X, y, batch_size, shuffle, predict): def _batch_generator(self): batch_index = self.sample_index[self.batch_size * self.counter:self.batch_size * (self.counter + 1)] - X_batch = self.X[batch_index, :] + X_batch = self.X[batch_index, :].toarray() if not self.predict: - y_batch = self.y[batch_index] + y_batch = self.y[batch_index].toarray() self.counter += 1 if self.counter == self.number_of_batches: if self.shuffle: np.random.shuffle(self.sample_index) self.counter = 0 if not self.predict: - return _sparse_to_sparse(X_batch), _sparse_to_sparse(y_batch) + return X_batch, y_batch else: - return _sparse_to_sparse(X_batch) + return X_batch + + +def mlp_soph(dropout): + return lambda X, y : mlp_soph_fn(X, y, dropout = dropout) -def mlp_soph(lr, dropout): - if lr is None: - # set lr to default option - lr = 0.1 - return lambda : (mlp_soph_fn, {"learning_rate": lr, "dropout" : dropout}) +#=============================================================================== +# def lstm(lr, dropout): +# if lr is None: +# # set lr to default option +# lr = 0.1 +# return lambda : (cnn_fn, {"learning_rate": lr, "dropout" : dropout}) +#=============================================================================== -def _sparse_to_sparse(X): - non_zero_rows, non_zero_cols = X.nonzero() - indices = np.array([[r, c] for r, c in zip(non_zero_rows, non_zero_cols)]) - values = np.array([X[r,c] for r, c in zip(non_zero_rows, non_zero_cols)]) - - shape = list(X.shape) - input_tensor = tf.SparseTensor(indices=indices, - values=values, - dense_shape=shape) - return input_tensor +#=============================================================================== +# def _sparse_to_sparse(X): +# non_zero_rows, non_zero_cols = X.nonzero() +# indices = np.array([[r, c] for r, c in zip(non_zero_rows, non_zero_cols)]) +# values = np.array([X[r,c] for r, c in zip(non_zero_rows, non_zero_cols)]) +# +# shape = list(X.shape) +# input_tensor = tf.SparseTensor(indices=indices, +# values=values, +# dense_shape=shape) +# return input_tensor +#=============================================================================== @@ -135,71 +153,153 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.1, 0.5)): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.5), threshold = 0.2, learning_rate = 0.1): """ """ + self.get_model = get_model + # enable early stopping on validation set self.validation_data_position = None # used by this class self.batch_size = batch_size self.num_epochs = num_epochs + self.threshold = 0.2 + if learning_rate is None: + self.learning_rate = self.batch_size / 512 * 0.01 + else: + self.learning_rate = learning_rate - # used by the tensorflow model function - print(get_model()) - self.model_fn, self.model_params = get_model() + # path to save the tensorflow model to + self._save_model_path = './multi-label-model' def fit(self, X, y): self.y = y - def model_fn_with_num_classes(features, targets, mode, params): - return self.model_fn(features, targets, mode, params, y.shape[1]) - - self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn_with_num_classes, params=self.model_params) - val_pos = self.validation_data_position - monitors = [] + if val_pos is not None: #callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')) #callbacks.append(ModelCheckpoint("weights.best.hdf5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')) X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] - + validation_batch_generator = BatchGenerator(X_val, y_val, X_val.shape[0], False, False) steps_per_epoch = int(np.ceil(X_train.shape[0] / self.batch_size)) - monitors.append(tf.contrib.learn.monitors.ValidationMonitor(input_fn = validation_batch_generator._batch_generator, - every_n_steps=steps_per_epoch, - early_stopping_metric="loss", - early_stopping_rounds=5 * steps_per_epoch, - eval_steps = int(np.ceil(X_val.shape[0] / self.batch_size)))) + #monitors.append(tf.contrib.learn.monitors.ValidationMonitor(input_fn = validation_batch_generator._batch_generator, + # every_n_steps=steps_per_epoch, + # early_stopping_metric="loss", + # early_stopping_rounds=5 * steps_per_epoch, + # eval_steps = int(np.ceil(X_val.shape[0] / self.batch_size)))) + # else: steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) X_train = X y_train = y - + + # Remove previous weights, bias, inputs, etc.. + tf.reset_default_graph() + + # Inputs + + # get_model has to return a + self.x_tensor, self.y_tensor, self.last_layer, self.params_fit, self.params_predict = self.get_model(X, y) + + # Name logits Tensor, so that is can be loaded from disk after training + #logits = tf.identity(logits, name='logits') + logits = tf.contrib.layers.linear(self.last_layer, + num_outputs=y.shape[1]) + + # Loss and Optimizer + cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y_tensor)) + optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(cost) + + # prediction + self.predictions = tf.contrib.layers.fully_connected(inputs=self.last_layer, + num_outputs=y.shape[1], + activation_fn=tf.sigmoid) + + session = tf.Session() + # Initializing the variables + session.run(tf.global_variables_initializer()) + batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) + # Training cycle + for epoch in range(self.num_epochs): + # Loop over all batches + for batch_i in range(steps_per_epoch): + X_batch, y_batch = batch_generator._batch_generator() + feed_dict = {self.x_tensor: X_batch, self.y_tensor: y_batch} + feed_dict.update(self.params_fit) + session.run(optimizer, feed_dict = feed_dict) + print('Epoch {:>2}, Batch {}: '.format(epoch + 1, batch_i), end='\r') + + self.session = session - self._estimator.fit(input_fn= batch_generator._batch_generator, steps=self.num_epochs * steps_per_epoch, monitors = monitors) + # Save model for prediction step + #saver = tf.train.Saver() + #saver.save(sess, self._save_model_path) + + #def model_fn_with_num_classes(features, targets, mode, params): + # return self.model_fn(features, targets, mode, params, y.shape[1]) + + #self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn_with_num_classes, params=self.model_params) + + #======================================================================= + # val_pos = self.validation_data_position + # monitors = [] + # if val_pos is not None: + # #callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')) + # #callbacks.append(ModelCheckpoint("weights.best.hdf5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')) + # X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] + # + # validation_batch_generator = BatchGenerator(X_val, y_val, X_val.shape[0], False, False) + # steps_per_epoch = int(np.ceil(X_train.shape[0] / self.batch_size)) + # monitors.append(tf.contrib.learn.monitors.ValidationMonitor(input_fn = validation_batch_generator._batch_generator, + # every_n_steps=steps_per_epoch, + # early_stopping_metric="loss", + # early_stopping_rounds=5 * steps_per_epoch, + # eval_steps = int(np.ceil(X_val.shape[0] / self.batch_size)))) + # else: + # steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) + # X_train = X + # y_train = y + # + # batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) + # + # self._estimator.fit(input_fn= batch_generator._batch_generator, steps=self.num_epochs * steps_per_epoch, monitors = monitors) + #======================================================================= def predict(self, X): - # TODO: implement batch prediction - prediction_steps = int(np.ceil(X.shape[0] / self.batch_size)) + session = self.session + #loaded_graph = tf.Graph() + # Load model + #loader = tf.train.import_meta_graph(self._save_model_path + '.meta') + #loader.restore(sess, self._save_model_path) + + # Get Tensors from loaded model + #loaded_x = loaded_graph.get_tensor_by_name('x:0') + #loaded_y = loaded_graph.get_tensor_by_name('y:0') + #loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0') + #loaded_logits = loaded_graph.get_tensor_by_name('logits:0') + #loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0' prediction = np.zeros((X.shape[0], self.y.shape[1])) + batch_generator = BatchGenerator(X, None, self.batch_size, False, True) + prediction_steps = int(np.ceil(X.shape[0] / self.batch_size)) for i in range(prediction_steps): - batch_generator = BatchGenerator(X, None, self.batch_size, False, True) - pred_generator = self._estimator.predict(input_fn=batch_generator._batch_generator) - pred_batch = np.array([single_prediction for single_prediction in pred_generator]) - prediction[i * self.batch_size:(i+1) * self.batch_size] = pred_batch + X_batch = batch_generator._batch_generator() + feed_dict = {self.x_tensor: X_batch} + feed_dict.update(self.params_predict) + pred_batch = session.run(self.predictions, feed_dict = feed_dict) + prediction[i * self.batch_size:(i+1) * self.batch_size, :] = pred_batch - threshold = 0.2 + threshold = self.threshold prediction[prediction > threshold] = 1 prediction[prediction <= threshold] = 0 return csr_matrix(prediction) - - diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index cb33080..43288ed 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -380,7 +380,8 @@ def create_classifier(options, num_concepts): "mlp": mlp, "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, - get_model = mlp_soph(options.learning_rate, options.dropout)), + learning_rate = options.learning_rate, + get_model = mlp_soph(options.dropout)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), From 7f8ef02afcddb96ded91753c66eaac5e17ea8cd8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 14:53:09 +0200 Subject: [PATCH 020/116] Adopt keras' categorical xentropy computation --- Code/lucid_ml/classifying/tensorflow_models.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index e85a463..2868816 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -214,8 +214,10 @@ def fit(self, X, y): num_outputs=y.shape[1]) # Loss and Optimizer - cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y_tensor)) - optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(cost) + losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y_tensor) + loss = tf.reduce_sum(losses, axis = 1) + loss = tf.reduce_mean(loss, axis = 0) + optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(loss) # prediction self.predictions = tf.contrib.layers.fully_connected(inputs=self.last_layer, From 9d100cbbcb0f7ed14046fb5c3152ed9d8d28adc8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 16:34:08 +0200 Subject: [PATCH 021/116] Fixed a bug in tensorflow prediction step --- .../lucid_ml/classifying/tensorflow_models.py | 23 ++++++++++--------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 2868816..4d3b344 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -166,7 +166,7 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.5), t # used by this class self.batch_size = batch_size self.num_epochs = num_epochs - self.threshold = 0.2 + self.threshold = threshold if learning_rate is None: self.learning_rate = self.batch_size / 512 * 0.01 else: @@ -216,13 +216,11 @@ def fit(self, X, y): # Loss and Optimizer losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y_tensor) loss = tf.reduce_sum(losses, axis = 1) - loss = tf.reduce_mean(loss, axis = 0) - optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(loss) + self.loss = tf.reduce_mean(loss, axis = 0) + optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) # prediction - self.predictions = tf.contrib.layers.fully_connected(inputs=self.last_layer, - num_outputs=y.shape[1], - activation_fn=tf.sigmoid) + self.predictions = tf.sigmoid(logits) session = tf.Session() # Initializing the variables @@ -237,9 +235,14 @@ def fit(self, X, y): feed_dict = {self.x_tensor: X_batch, self.y_tensor: y_batch} feed_dict.update(self.params_fit) session.run(optimizer, feed_dict = feed_dict) - print('Epoch {:>2}, Batch {}: '.format(epoch + 1, batch_i), end='\r') + + # overwrite some values like dropout for prediction + feed_dict.update(self.params_predict) + loss = session.run(self.loss, feed_dict = feed_dict) + print('Epoch {:>2}, Batch {}, Loss: {} '.format(epoch + 1, batch_i + 1, loss), end='\r') self.session = session + print('') # Save model for prediction step #saver = tf.train.Saver() @@ -301,7 +304,5 @@ def predict(self, X): pred_batch = session.run(self.predictions, feed_dict = feed_dict) prediction[i * self.batch_size:(i+1) * self.batch_size, :] = pred_batch - threshold = self.threshold - prediction[prediction > threshold] = 1 - prediction[prediction <= threshold] = 0 - return csr_matrix(prediction) + result = csr_matrix(prediction > self.threshold) + return result From a0136a99bbbbaa9412c2b3263d7f4c19b0a013c9 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 17:16:35 +0200 Subject: [PATCH 022/116] Compute validation loss in tensorflow-models --- .../lucid_ml/classifying/tensorflow_models.py | 49 ++++++++++++------- 1 file changed, 31 insertions(+), 18 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 4d3b344..a1f363b 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -3,8 +3,9 @@ from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib from scipy.sparse.csr import csr_matrix from sklearn.base import BaseEstimator +import math -tf.logging.set_verbosity(tf.logging.INFO) +#tf.logging.set_verbosity(tf.logging.INFO) #=============================================================================== # def cnn_fn(features, targets, mode, params, num_classes): @@ -174,8 +175,10 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.5), t # path to save the tensorflow model to self._save_model_path = './multi-label-model' - - + + def _calc_num_steps(self, X): + return int(np.ceil(X.shape[0] / self.batch_size)) + def fit(self, X, y): self.y = y @@ -183,20 +186,13 @@ def fit(self, X, y): val_pos = self.validation_data_position if val_pos is not None: - #callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')) - #callbacks.append(ModelCheckpoint("weights.best.hdf5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')) - X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] + X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:, :] validation_batch_generator = BatchGenerator(X_val, y_val, X_val.shape[0], False, False) - steps_per_epoch = int(np.ceil(X_train.shape[0] / self.batch_size)) - #monitors.append(tf.contrib.learn.monitors.ValidationMonitor(input_fn = validation_batch_generator._batch_generator, - # every_n_steps=steps_per_epoch, - # early_stopping_metric="loss", - # early_stopping_rounds=5 * steps_per_epoch, - # eval_steps = int(np.ceil(X_val.shape[0] / self.batch_size)))) - # + validation_predictions = self._calc_num_steps(X_val) + steps_per_epoch = self._calc_num_steps(X_train) else: - steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) + steps_per_epoch = self._calc_num_steps(X) X_train = X y_train = y @@ -228,6 +224,7 @@ def fit(self, X, y): batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) # Training cycle + avg_validation_loss = math.inf for epoch in range(self.num_epochs): # Loop over all batches for batch_i in range(steps_per_epoch): @@ -236,10 +233,26 @@ def fit(self, X, y): feed_dict.update(self.params_fit) session.run(optimizer, feed_dict = feed_dict) - # overwrite some values like dropout for prediction + # overwrite parameter values for prediction step feed_dict.update(self.params_predict) - loss = session.run(self.loss, feed_dict = feed_dict) - print('Epoch {:>2}, Batch {}, Loss: {} '.format(epoch + 1, batch_i + 1, loss), end='\r') + loss = session.run(self.loss, feed_dict = feed_dict) + + # calculate validation loss at end of epoch if early stopping is on + if batch_i + 1 == steps_per_epoch and val_pos is not None: + validation_losses = [] + weights = [] + for _ in range(validation_predictions): + X_val_batch, y_val_batch = validation_batch_generator._batch_generator() + weights.append(X_val_batch.shape[0]) + feed_dict = {self.x_tensor: X_val_batch, self.y_tensor: y_val_batch} + feed_dict.update(self.params_predict) + validation_losses.append(session.run(self.loss, feed_dict = feed_dict)) + avg_validation_loss = np.average(np.array(validation_losses), weights = np.array(weights)) + + # print progress + print('Epoch {:>2}/{}, Batch {}/{}, Loss: {}, Validation-Loss: {}'.format(epoch + 1, self.num_epochs, + batch_i + 1, steps_per_epoch, + loss, avg_validation_loss), end='\r') self.session = session print('') @@ -296,7 +309,7 @@ def predict(self, X): prediction = np.zeros((X.shape[0], self.y.shape[1])) batch_generator = BatchGenerator(X, None, self.batch_size, False, True) - prediction_steps = int(np.ceil(X.shape[0] / self.batch_size)) + prediction_steps = self._calc_num_steps(X) for i in range(prediction_steps): X_batch = batch_generator._batch_generator() feed_dict = {self.x_tensor: X_batch} From 402ce24201890098206eb71233b19f6f08390ae3 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 17:43:32 +0200 Subject: [PATCH 023/116] Save and use best model in terms of validation loss for prediction --- .../lucid_ml/classifying/tensorflow_models.py | 25 +++++++++++-------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index a1f363b..606d809 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -174,7 +174,7 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.5), t self.learning_rate = learning_rate # path to save the tensorflow model to - self._save_model_path = './multi-label-model' + self._save_model_path = './best-model' def _calc_num_steps(self, X): return int(np.ceil(X.shape[0] / self.batch_size)) @@ -225,6 +225,7 @@ def fit(self, X, y): batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) # Training cycle avg_validation_loss = math.inf + best_validation_loss = math.inf for epoch in range(self.num_epochs): # Loop over all batches for batch_i in range(steps_per_epoch): @@ -249,17 +250,20 @@ def fit(self, X, y): validation_losses.append(session.run(self.loss, feed_dict = feed_dict)) avg_validation_loss = np.average(np.array(validation_losses), weights = np.array(weights)) + if avg_validation_loss < best_validation_loss: + # save model + # Save model for prediction step + best_validation_loss = avg_validation_loss + saver = tf.train.Saver() + saver.save(session, self._save_model_path) + # print progress - print('Epoch {:>2}/{}, Batch {}/{}, Loss: {}, Validation-Loss: {}'.format(epoch + 1, self.num_epochs, + print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Loss: {:0.4f}, Best Validation-Loss: {:0.4f}'.format(epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, - loss, avg_validation_loss), end='\r') + loss, avg_validation_loss, best_validation_loss), end='\r') self.session = session print('') - - # Save model for prediction step - #saver = tf.train.Saver() - #saver.save(sess, self._save_model_path) #def model_fn_with_num_classes(features, targets, mode, params): # return self.model_fn(features, targets, mode, params, y.shape[1]) @@ -296,9 +300,10 @@ def predict(self, X): session = self.session #loaded_graph = tf.Graph() - # Load model - #loader = tf.train.import_meta_graph(self._save_model_path + '.meta') - #loader.restore(sess, self._save_model_path) + if self.validation_data_position: + # Load model + loader = tf.train.import_meta_graph(self._save_model_path + '.meta') + loader.restore(self.session, self._save_model_path) # Get Tensors from loaded model #loaded_x = loaded_graph.get_tensor_by_name('x:0') From e475117541efb0aefd7660d94d82905a64d682cb Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 18:01:03 +0200 Subject: [PATCH 024/116] Keep TF-reimplementation of keras mlp as 'mlpbase' --- Code/lucid_ml/classifying/tensorflow_models.py | 8 ++++---- Code/lucid_ml/run.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 606d809..7d0d3fa 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -65,7 +65,7 @@ # loss=loss, # train_op=train_op) #=============================================================================== -def mlp_soph_fn(X, y, dropout = 0.5): +def mlp_base_fn(X, y, dropout = 0.5): """Model function for MLP-Soph.""" # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") @@ -114,8 +114,8 @@ def _batch_generator(self): return X_batch -def mlp_soph(dropout): - return lambda X, y : mlp_soph_fn(X, y, dropout = dropout) +def mlp_base(dropout): + return lambda X, y : mlp_base_fn(X, y, dropout = dropout) #=============================================================================== @@ -154,7 +154,7 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_soph(0.5), threshold = 0.2, learning_rate = 0.1): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1): """ """ diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 43288ed..dd8f8d6 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -39,7 +39,7 @@ from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack -from classifying.tensorflow_models import MultiLabelSKFlow, mlp_soph +from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -378,10 +378,10 @@ def create_classifier(options, num_concepts): "rocchiodt": ClassifierStack(base_classifier=RocchioClassifier(metric = 'cosine'), n_jobs=options.jobs, n=options.k), "logregressdt": ClassifierStack(base_classifier=logregress, n_jobs=options.jobs, n=options.k), "mlp": mlp, - "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, + "mlpbase" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, - get_model = mlp_soph(options.dropout)), + get_model = mlp_base(options.dropout)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -509,7 +509,7 @@ def _generate_parsers(): classifier_options.add_argument('-f', '--classifier', dest="clf_key", default="nn", help= "Specify the final classifier.", choices=["nn", "brknna", "brknnb", "bbayes", "mbayes", "lsvc", "sgd", "sgddt", "rocchio", "rocchiodt", "logregress", "logregressdt", - "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpsoph']) + "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase']) classifier_options.add_argument('-a', '--alpha', dest="alpha", type=float, default=1e-7, help= \ "Specify alpha parameter for stochastic gradient descent") classifier_options.add_argument('-n', dest="k", type=int, default=1, help= From c06434ec74b2e434c073f880914e97b3b9f25d2a Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 18:20:27 +0200 Subject: [PATCH 025/116] MLP-Soph now has lookup table similar to fastText --- .../lucid_ml/classifying/tensorflow_models.py | 32 +++++++++++++++++-- Code/lucid_ml/run.py | 10 ++++-- 2 files changed, 37 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 7d0d3fa..4f8cf30 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -82,6 +82,35 @@ def mlp_base_fn(X, y, dropout = 0.5): return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict + +def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30): + """Model function for MLP-Soph.""" + # convert sparse tensors to dense + x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") + y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") + dropout_tensor = tf.placeholder(tf.float32, name = "dropout") + + params_fit = {dropout_tensor : dropout} + params_predict = {dropout_tensor : 1} + + # apply a look-up as described by the fastText paper + lookup_table = tf.Variable(tf.truncated_normal([X.shape[1], embedding_size], mean=0.0, stddev=0.1)) + embedding_layer = tf.matmul(x_tensor, lookup_table) + + # Connect the embedding layer to hidden layer + # (features) with relu activation and add dropout + hidden_layer = tf.contrib.layers.relu(embedding_layer, 1000) + hidden_dropout = tf.nn.dropout(hidden_layer, dropout_tensor) + + return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict + +def mlp_base(dropout): + return lambda X, y : mlp_base_fn(X, y, dropout = dropout) + +def mlp_soph(dropout, embedding_size): + return lambda X, y : mlp_soph_fn(X, y, dropout = dropout, embedding_size = embedding_size) + + class BatchGenerator: def __init__(self, X, y, batch_size, shuffle, predict): @@ -114,9 +143,6 @@ def _batch_generator(self): return X_batch -def mlp_base(dropout): - return lambda X, y : mlp_base_fn(X, y, dropout = dropout) - #=============================================================================== # def lstm(lr, dropout): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index dd8f8d6..0e952d3 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -39,7 +39,7 @@ from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack -from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base +from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base, mlp_soph from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -382,6 +382,10 @@ def create_classifier(options, num_concepts): num_epochs=options.max_iterations, learning_rate = options.learning_rate, get_model = mlp_base(options.dropout)), + "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, + num_epochs=options.max_iterations, + learning_rate = options.learning_rate, + get_model = mlp_soph(options.dropout, options.embedding_size)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -509,7 +513,7 @@ def _generate_parsers(): classifier_options.add_argument('-f', '--classifier', dest="clf_key", default="nn", help= "Specify the final classifier.", choices=["nn", "brknna", "brknnb", "bbayes", "mbayes", "lsvc", "sgd", "sgddt", "rocchio", "rocchiodt", "logregress", "logregressdt", - "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase']) + "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase', "mlpsoph"]) classifier_options.add_argument('-a', '--alpha', dest="alpha", type=float, default=1e-7, help= \ "Specify alpha parameter for stochastic gradient descent") classifier_options.add_argument('-n', dest="k", type=int, default=1, help= @@ -545,6 +549,8 @@ def _generate_parsers(): neural_network_options = parser.add_argument_group("Neural Network Options") neural_network_options.add_argument('--dropout', type=float, dest="dropout", default=0.5, help= "Determine the keep probability for all dropout layers.") + neural_network_options.add_argument('--embedding_size', type=int, dest="embedding_size", default=300, help= + "Determine the size of a word embedding vector (for MLP-Soph, CNN, and LSTM if embedding is learned jointly). [300]") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 5bc5baa78b652689030225b2fa3457f7372a23e0 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 19:39:30 +0200 Subject: [PATCH 026/116] Skip embedding layer by specifying embedding_size=0 --- Code/lucid_ml/classifying/tensorflow_models.py | 10 ++++++++-- Code/lucid_ml/run.py | 3 ++- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 4f8cf30..e9f6aa8 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -94,8 +94,11 @@ def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30): params_predict = {dropout_tensor : 1} # apply a look-up as described by the fastText paper - lookup_table = tf.Variable(tf.truncated_normal([X.shape[1], embedding_size], mean=0.0, stddev=0.1)) - embedding_layer = tf.matmul(x_tensor, lookup_table) + if embedding_size > 0: + lookup_table = tf.Variable(tf.truncated_normal([X.shape[1], embedding_size], mean=0.0, stddev=0.1)) + embedding_layer = tf.matmul(x_tensor, lookup_table) + else: + embedding_layer = x_tensor # Connect the embedding layer to hidden layer # (features) with relu activation and add dropout @@ -349,4 +352,7 @@ def predict(self, X): prediction[i * self.batch_size:(i+1) * self.batch_size, :] = pred_batch result = csr_matrix(prediction > self.threshold) + + # close the session, since no longer needed + session.close() return result diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 0e952d3..96644a3 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -550,7 +550,8 @@ def _generate_parsers(): neural_network_options.add_argument('--dropout', type=float, dest="dropout", default=0.5, help= "Determine the keep probability for all dropout layers.") neural_network_options.add_argument('--embedding_size', type=int, dest="embedding_size", default=300, help= - "Determine the size of a word embedding vector (for MLP-Soph, CNN, and LSTM if embedding is learned jointly). [300]") + "Determine the size of a word embedding vector (for MLP-Soph, CNN, and LSTM if embedding is learned jointly). \ + Specify --embedding_size=0 to skip the embedding layer, if applicable. [300]") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 3e7f0d956139a855710269522254654b9064127f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 20:25:46 +0200 Subject: [PATCH 027/116] Self normalizing neural networks for MLP-Soph --- .../lucid_ml/classifying/tensorflow_models.py | 76 ++++++++++++++++--- Code/lucid_ml/run.py | 8 +- 2 files changed, 73 insertions(+), 11 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index e9f6aa8..1a2033e 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -1,9 +1,11 @@ import numpy as np import tensorflow as tf -from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib from scipy.sparse.csr import csr_matrix from sklearn.base import BaseEstimator -import math +import math, numbers +from tensorflow.python.framework import ops, tensor_shape, tensor_util +from tensorflow.python.ops import math_ops, random_ops, array_ops +from tensorflow.python.layers import utils #tf.logging.set_verbosity(tf.logging.INFO) @@ -82,8 +84,52 @@ def mlp_base_fn(X, y, dropout = 0.5): return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict +# https://github.com/bioinf-jku/SNNs/blob/master/selu.py +def dropout_selu(x, rate, alpha= -1.7580993408473766, fixedPointMean=0.0, fixedPointVar=1.0, + noise_shape=None, seed=None, name=None, training=False): + """Dropout to a value with rescaling.""" -def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30): + def dropout_selu_impl(x, rate, alpha, noise_shape, seed, name): + keep_prob = 1.0 - rate + x = ops.convert_to_tensor(x, name="x") + if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1: + raise ValueError("keep_prob must be a scalar tensor or a float in the " + "range (0, 1], got %g" % keep_prob) + keep_prob = ops.convert_to_tensor(keep_prob, dtype=x.dtype, name="keep_prob") + keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar()) + + alpha = ops.convert_to_tensor(alpha, dtype=x.dtype, name="alpha") + alpha.get_shape().assert_is_compatible_with(tensor_shape.scalar()) + + if tensor_util.constant_value(keep_prob) == 1: + return x + + noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x) + random_tensor = keep_prob + random_tensor += random_ops.random_uniform(noise_shape, seed=seed, dtype=x.dtype) + binary_tensor = math_ops.floor(random_tensor) + ret = x * binary_tensor + alpha * (1-binary_tensor) + + a = math_ops.sqrt(fixedPointVar / (keep_prob *((1-keep_prob) * math_ops.pow(alpha-fixedPointMean,2) + fixedPointVar))) + + b = fixedPointMean - a * (keep_prob * fixedPointMean + (1 - keep_prob) * alpha) + ret = a * ret + b + ret.set_shape(x.get_shape()) + return ret + + with ops.name_scope(name, "dropout", [x]) as name: + return utils.smart_cond(training, + lambda: dropout_selu_impl(x, rate, alpha, noise_shape, seed, name), + lambda: array_ops.identity(x)) + +# https://github.com/bioinf-jku/SNNs/blob/master/selu.py +def selu(x): + with ops.name_scope('elu') as scope: + alpha = 1.6732632423543772848170429916717 + scale = 1.0507009873554804934193349852946 + return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x)) + +def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = True): """Model function for MLP-Soph.""" # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") @@ -100,18 +146,28 @@ def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30): else: embedding_layer = x_tensor - # Connect the embedding layer to hidden layer - # (features) with relu activation and add dropout - hidden_layer = tf.contrib.layers.relu(embedding_layer, 1000) - hidden_dropout = tf.nn.dropout(hidden_layer, dropout_tensor) + # Connect the embedding layer to the hidden layers + # (features) with relu activation and add dropout everywhere + hidden_layer = embedding_layer + for hidden_units in hidden_layers: + if not self_normalizing: + hidden_layer = tf.contrib.layers.relu(hidden_layer, hidden_units) + hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) + else: + hidden_layer = tf.contrib.layers.fully_connected(hidden_layer, hidden_units, + activation_fn=None, + weights_initializer=tf.contrib.layers.variance_scaling_initializer(factor=1.0, mode='FAN_IN')) + hidden_layer = selu(hidden_layer) + hidden_layer = dropout_selu(hidden_layer, dropout_tensor) - return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict + return x_tensor, y_tensor, hidden_layer, params_fit, params_predict def mlp_base(dropout): return lambda X, y : mlp_base_fn(X, y, dropout = dropout) -def mlp_soph(dropout, embedding_size): - return lambda X, y : mlp_soph_fn(X, y, dropout = dropout, embedding_size = embedding_size) +def mlp_soph(dropout, embedding_size, hidden_layers, self_normalizing): + return lambda X, y : mlp_soph_fn(X, y, dropout = dropout, embedding_size = embedding_size, + hidden_layers = hidden_layers, self_normalizing = self_normalizing) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 96644a3..28ed6a3 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -385,7 +385,8 @@ def create_classifier(options, num_concepts): "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, - get_model = mlp_soph(options.dropout, options.embedding_size)), + get_model = mlp_soph(options.dropout, options.embedding_size, + hidden_layers = options.hidden_layers, self_normalizing = options.snn)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -552,6 +553,11 @@ def _generate_parsers(): neural_network_options.add_argument('--embedding_size', type=int, dest="embedding_size", default=300, help= "Determine the size of a word embedding vector (for MLP-Soph, CNN, and LSTM if embedding is learned jointly). \ Specify --embedding_size=0 to skip the embedding layer, if applicable. [300]") + neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= + "Specify the number of layers and the respective number of units as a list. The i-th element of the list \ + specifies the number of units in layer i. [1000]") + neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= + "Whether to use SELU activation and -dropout. If set to False, RELU activation is used. [False]") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 691485bbe52b4e57dc082468c864e74db66da641 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 22:45:37 +0200 Subject: [PATCH 028/116] Bugfix: unable to use 'combined' dataset format with -xX --- Code/lucid_ml/run.py | 2 +- Code/lucid_ml/utils/Extractor.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 28ed6a3..26b44ee 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -66,7 +66,7 @@ def run(options): if options.fixed_folds: X_raw, Y_raw, tr, fold_list = load_dataset(DATA_PATHS, options.data_key, options.fulltext, fixed_folds=True) else: - X_raw, Y_raw, tr = load_dataset(DATA_PATHS, options.data_key, options.fulltext, fixed_folds=False) + X_raw, Y_raw, tr, _ = load_dataset(DATA_PATHS, options.data_key, options.fulltext, fixed_folds=False) if options.toy_size < 1: if VERBOSE: print("Just toying with %d%% of the data." % (options.toy_size * 100)) zipped = list(zip(X_raw, Y_raw)) diff --git a/Code/lucid_ml/utils/Extractor.py b/Code/lucid_ml/utils/Extractor.py index 048680d..a5bfb88 100644 --- a/Code/lucid_ml/utils/Extractor.py +++ b/Code/lucid_ml/utils/Extractor.py @@ -65,7 +65,7 @@ def load_dataset(DATA_PATHS, key='econ62k', fulltext=False, fixed_folds = False) data_list, gold_list = reduce_dicts([data, gold]) tr = ThesaurusReader(DATA_PATHS[key]['thes']) - return data_list, gold_list, tr + return data_list, gold_list, tr, None elif dataset_format == "combined": # extract the available folds and keep each folds samples in a separate list From d5a90fc6a7b39078daa5c546b100f7461599f511 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 21 Jun 2017 23:13:20 +0200 Subject: [PATCH 029/116] Use --fixed_folds and --folds=1 to do only a one fold evaluation with fixed folds --- Code/lucid_ml/run.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 26b44ee..c96c1c6 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -190,7 +190,9 @@ def run(options): kf = KFold(n_splits=options.folds, shuffle=True) elif options.fixed_folds: fixed_folds = [] - basic_folds = range(options.folds) + + # TODO: we assume 10 normal folds and 1 folds with extra samples. need to generalize + basic_folds = range(10) # we assume the extra data to be in the last fold extra_data = [index for index,x in enumerate(fold_list) if x == options.folds] From 289bf0d84e14a7d8a79253dbdb0e3ae64e123c9c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 22 Jun 2017 21:32:29 +0200 Subject: [PATCH 030/116] Quick-fix for extra-samples not being sampled from the 11th fold --- Code/lucid_ml/run.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index c96c1c6..a324555 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -195,7 +195,8 @@ def run(options): basic_folds = range(10) # we assume the extra data to be in the last fold - extra_data = [index for index,x in enumerate(fold_list) if x == options.folds] + # TODO: currently we assume 10 folds (+1 extra) + extra_data = [index for index,x in enumerate(fold_list) if x == 10] validation_set_indices = [] for i in range(options.folds): From 1ff4c28f76020a8d133fc66efd1b130903eb7b95 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 22 Jun 2017 21:52:27 +0200 Subject: [PATCH 031/116] Stop after 5 rounds of no improvement in validation loss --- Code/lucid_ml/classifying/tensorflow_models.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 1a2033e..f2e9530 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -239,7 +239,7 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5): """ """ @@ -250,6 +250,7 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), t self.validation_data_position = None # used by this class + self.tolerance = tolerance self.batch_size = batch_size self.num_epochs = num_epochs self.threshold = threshold @@ -311,7 +312,12 @@ def fit(self, X, y): # Training cycle avg_validation_loss = math.inf best_validation_loss = math.inf + epochs_of_no_improvement = 0 for epoch in range(self.num_epochs): + + if val_pos is not None and epochs_of_no_improvement == self.tolerance: + break + # Loop over all batches for batch_i in range(steps_per_epoch): X_batch, y_batch = batch_generator._batch_generator() @@ -341,6 +347,12 @@ def fit(self, X, y): best_validation_loss = avg_validation_loss saver = tf.train.Saver() saver.save(session, self._save_model_path) + epochs_of_no_improvement = 0 + else: + epochs_of_no_improvement += 1 + if epochs_of_no_improvement > self.tolerance: + print("No improvement in validation loss for", self.tolerance, "epochs. Stopping early.") + break # print progress print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Loss: {:0.4f}, Best Validation-Loss: {:0.4f}'.format(epoch + 1, self.num_epochs, From 37f4c9bbafeecb5278c2539a6dcf7e816bb27c1d Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 26 Jun 2017 16:06:27 +0200 Subject: [PATCH 032/116] Made clearer where 'dropout' means keep probability. --- Code/lucid_ml/classifying/tensorflow_models.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index f2e9530..63c0083 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -67,14 +67,14 @@ # loss=loss, # train_op=train_op) #=============================================================================== -def mlp_base_fn(X, y, dropout = 0.5): +def mlp_base_fn(X, y, keep_prob_dropout = 0.5): """Model function for MLP-Soph.""" # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") - params_fit = {dropout_tensor : dropout} + params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} # Connect the first hidden layer to input layer @@ -129,14 +129,14 @@ def selu(x): scale = 1.0507009873554804934193349852946 return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x)) -def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = True): +def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = True): """Model function for MLP-Soph.""" # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") - params_fit = {dropout_tensor : dropout} + params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} # apply a look-up as described by the fastText paper @@ -162,11 +162,11 @@ def mlp_soph_fn(X, y, dropout = 0.5, embedding_size = 30, hidden_layers = [1000] return x_tensor, y_tensor, hidden_layer, params_fit, params_predict -def mlp_base(dropout): - return lambda X, y : mlp_base_fn(X, y, dropout = dropout) +def mlp_base(keep_prob_dropout): + return lambda X, y : mlp_base_fn(X, y, keep_prob_dropout = keep_prob_dropout) -def mlp_soph(dropout, embedding_size, hidden_layers, self_normalizing): - return lambda X, y : mlp_soph_fn(X, y, dropout = dropout, embedding_size = embedding_size, +def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing): + return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, self_normalizing = self_normalizing) From e40ad5a929b5633889e2660426b94fae68a09a21 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 28 Jun 2017 23:26:04 +0200 Subject: [PATCH 033/116] Escaped code that would cause crash if no thesaurus is provided --- Code/lucid_ml/run.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index a324555..8a3e8fa 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -83,14 +83,17 @@ def run(options): # --- EXTRACT FEATURES --- input_format = 'filename' if options.fulltext else 'content' - concept_analyzer = SynsetAnalyzer().analyze if options.synsets \ - else ConceptAnalyzer(tr.thesaurus, input=input_format, persist=options.persist and options.concepts, - persist_dir=options.persist_to, repersist=options.repersist, - file_path=DATA_PATHS[options.data_key]['X']).analyze + if options.concepts: + if tr is None: + raise ValueError("Unable to extract concepts, since no thesaurus is given!") + concept_analyzer = SynsetAnalyzer().analyze if options.synsets \ + else ConceptAnalyzer(tr.thesaurus, input=input_format, persist=options.persist and options.concepts, + persist_dir=options.persist_to, repersist=options.repersist, + file_path=DATA_PATHS[options.data_key]['X']).analyze + concepts = CountVectorizer(input=input_format, analyzer=concept_analyzer, binary=options.binary, + vocabulary=tr.nodename_index if not options.synsets else None) terms = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, token_pattern=word_regexp, max_features=options.max_features) - concepts = CountVectorizer(input=input_format, analyzer=concept_analyzer, binary=options.binary, - vocabulary=tr.nodename_index if not options.synsets else None) if options.hierarchical: hierarchy = tr.nx_graph From 3d72aad9d49687af81ef13f03f930f745d0f9e6a Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 28 Jun 2017 23:27:17 +0200 Subject: [PATCH 034/116] May specify 'label_delimiter' in data json file to tell how to split up the labels in the 'combined' format --- Code/lucid_ml/utils/Extractor.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/utils/Extractor.py b/Code/lucid_ml/utils/Extractor.py index a5bfb88..efe16b2 100644 --- a/Code/lucid_ml/utils/Extractor.py +++ b/Code/lucid_ml/utils/Extractor.py @@ -8,10 +8,10 @@ from utils.thesaurus_reader import ThesaurusReader -def split_labels(labels_string): - return labels_string.split(",") +def split_labels(labels_string, delimiter = ","): + return labels_string.split(delimiter) -def load_data(df, fulltext, fixed_folds): +def load_data(df, fulltext, fixed_folds, label_delimiter = ","): if fulltext: content_column = "fulltext_path" @@ -26,7 +26,7 @@ def load_data(df, fulltext, fixed_folds): # bring goldstandard in dictionary format gold = dict() for row in df.iterrows(): - gold[row[1]["id"]] = split_labels(row[1]["labels"]) + gold[row[1]["id"]] = split_labels(row[1]["labels"], delimiter = label_delimiter) # by default, there is only one fold folds = dict() @@ -70,7 +70,7 @@ def load_dataset(DATA_PATHS, key='econ62k', fulltext=False, fixed_folds = False) elif dataset_format == "combined": # extract the available folds and keep each folds samples in a separate list df = pd.read_csv(data_set["X"]) - data_list, gold_list, fold_list = load_data(df, fulltext, fixed_folds) + data_list, gold_list, fold_list = load_data(df, fulltext, fixed_folds, label_delimiter = "," if "label_delimiter" not in data_set else data_set["label_delimiter"]) tr = None if "thes" not in data_set else ThesaurusReader(data_set['thes']) return data_list, gold_list, tr, fold_list From 16accd020ef0d91124730c0aca923eeba5676c2f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 29 Jun 2017 18:46:33 +0200 Subject: [PATCH 035/116] Added first version of CNN (Kim's sentence classification) --- .../lucid_ml/classifying/tensorflow_models.py | 117 +++++++++--------- Code/lucid_ml/run.py | 29 +++-- 2 files changed, 78 insertions(+), 68 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 63c0083..7a24693 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -9,64 +9,54 @@ #tf.logging.set_verbosity(tf.logging.INFO) -#=============================================================================== -# def cnn_fn(features, targets, mode, params, num_classes): -# """Model function for CNN.""" -# # convert sparse tensors to dense -# -# EMBEDDING_SIZE = 50 -# -# W = tf.Variable(tf.random_uniform([vocab_size, EMBEDDING_SIZE], -1.0, 1.0), -# name="W") -# self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x) -# self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1) -# -# if mode != tf.contrib.learn.ModeKeys.INFER: -# targets = tf.sparse_reorder(targets) -# targets = tf.sparse_tensor_to_dense(targets) -# targets = tf.to_float(targets) -# targets = tf.reshape(targets, [num_samples, num_classes]) -# -# # Connect the first hidden layer to input layer -# # (features) with relu activation and add dropout -# hidden_layer = tf.contrib.layers.relu(features, 1000) -# hidden_dropout = tf.nn.dropout(hidden_layer, params["dropout"]) -# -# -# # Connect the output layer to second hidden layer (no activation fn) -# -# output_layer = tf.contrib.layers.linear(hidden_dropout, -# num_outputs=num_classes) -# -# output_layer = tf.to_float(output_layer) -# -# # Reshape output layer to 1-dim Tensor to return predictions -# #predictions = tf.reshape(output_layer, [-1]) -# predictions = tf.contrib.layers.fully_connected(inputs=hidden_dropout, -# num_outputs=num_classes, -# activation_fn=tf.sigmoid) -# -# # Calculate loss using mean squared error -# if mode != tf.contrib.learn.ModeKeys.INFER: -# losses = tf.nn.sigmoid_cross_entropy_with_logits(labels = targets, logits = output_layer) -# loss = tf.reduce_sum(losses) -# else: -# loss = tf.constant(0.) -# -# if mode != tf.contrib.learn.ModeKeys.INFER: -# train_op = tf.contrib.layers.optimize_loss( -# loss=loss, -# global_step=tf.contrib.framework.get_global_step(), -# learning_rate=params["learning_rate"], -# optimizer="Adam") -# else: -# train_op = None -# return model_fn_lib.ModelFnOps( -# mode=mode, -# predictions=predictions, -# loss=loss, -# train_op=train_op) -#=============================================================================== +def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): + """Model function for CNN.""" + + # set vocab size to the largest word identifier that exists in the training data + 1 + vocab_size = int(np.max(X)) + 1 + sequence_length = X.shape[1] + x_tensor = tf.placeholder(tf.int32, shape=(None, sequence_length), name = "x") + y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") + dropout_tensor = tf.placeholder(tf.float32, name = "dropout") + + params_fit = {dropout_tensor : 1 - keep_prob_dropout} + params_predict = {dropout_tensor : 1} + + with tf.device('/cpu:0'), tf.name_scope("embedding"): + lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), + name="W") + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) + + # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling + embedded_words = tf.expand_dims(embedded_words, -1) + + # these are set according to Kim's Sentence Classification + window_sizes = [3, 4, 5] + num_filters = 100 + stride = [1, 1, 1, 1] + padding = "VALID" + + pooled_outputs = [] + for window_size in window_sizes: + filter_weights = tf.Variable(tf.random_normal([window_size, embedding_size, 1, num_filters], stddev=0.1)) + conv = tf.nn.conv2d(embedded_words, filter_weights, stride, padding) + bias = tf.Variable(tf.random_normal([num_filters])) + detector = tf.nn.relu(tf.nn.bias_add(conv, bias)) + pooling = tf.nn.max_pool(detector, + ksize = [1, sequence_length - window_size + 1, 1, 1], + strides = stride, + padding = "VALID") + pooled_outputs.append(tf.reshape(pooling, [-1, num_filters])) + + + concatenated_pools = tf.concat(pooled_outputs, 1) + num_filters_total = num_filters * len(window_sizes) + hidden_layer = tf.reshape(concatenated_pools, [-1, num_filters_total]) + + + hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) + + return x_tensor, y_tensor, hidden_layer, params_fit, params_predict def mlp_base_fn(X, y, keep_prob_dropout = 0.5): """Model function for MLP-Soph.""" # convert sparse tensors to dense @@ -168,6 +158,10 @@ def mlp_base(keep_prob_dropout): def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing): return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, self_normalizing = self_normalizing) + +def cnn(keep_prob_dropout, embedding_size, hidden_layers): + return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, + hidden_layers = hidden_layers) class BatchGenerator: @@ -188,7 +182,11 @@ def __init__(self, X, y, batch_size, shuffle, predict): def _batch_generator(self): batch_index = self.sample_index[self.batch_size * self.counter:self.batch_size * (self.counter + 1)] - X_batch = self.X[batch_index, :].toarray() + + X_batch = self.X[batch_index, :] + if type(X_batch) != np.ndarray: + X_batch.toarray() + if not self.predict: y_batch = self.y[batch_index].toarray() self.counter += 1 @@ -266,7 +264,6 @@ def _calc_num_steps(self, X): return int(np.ceil(X.shape[0] / self.batch_size)) def fit(self, X, y): - self.y = y val_pos = self.validation_data_position diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 8a3e8fa..9cf825b 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -39,7 +39,7 @@ from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack -from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base, mlp_soph +from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base, mlp_soph, cnn from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -49,6 +49,7 @@ from weighting.bm25transformer import BM25Transformer from weighting.concept_analysis import ConceptAnalyzer from weighting.graph_score_vectorizer import GraphVectorizer +from utils.text_encoding import TextEncoder import scipy.sparse as sps logging.basicConfig(level=logging.INFO, @@ -127,8 +128,12 @@ def run(options): extractor = FeatureUnion([("terms", terms), ("concepts", concepts)]) elif options.terms: extractor = terms - else: + elif options.concepts: extractor = concepts + elif options.onehot: + extractor = TextEncoder(input_format = "filename" if options.fulltext else "content") + else: + raise ValueError("No feature representation specified!") if VERBOSE: print("Extracting features...") if VERBOSE > 1: start_ef = default_timer() @@ -137,7 +142,7 @@ def run(options): if options.persist: persister.persist(X, Y, tr) - if VERBOSE: + if VERBOSE > 3: print("X = " + repr(X)) print("Vocabulary size: {}".format(X.shape[1])) print("Number of documents: {}".format(X.shape[0])) @@ -265,7 +270,7 @@ def split(self, X): Y_train = np.vstack((Y_train, Y_val)) # mlp doesn't seem to like being stuck into a new process... - if options.debug or options.clf_key in {'mlp', 'mlpthr', 'mlpsoph'}: + if options.debug or options.clf_key in {'mlp', 'mlpthr', 'mlpsoph', "cnn", "mlpbase"}: Y_pred, Y_train_pred = fit_predict(X_test, X_train, Y_train, options, tr, clf) else: Y_pred, Y_train_pred = fit_predict_new_process(X_test, X_train, Y_train, options, tr, clf) @@ -393,6 +398,11 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, get_model = mlp_soph(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, self_normalizing = options.snn)), + "cnn": MultiLabelSKFlow(batch_size = options.batch_size, + num_epochs=options.max_iterations, + learning_rate = options.learning_rate, + get_model = cnn(options.dropout, options.embedding_size, + hidden_layers = options.hidden_layers)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -405,7 +415,7 @@ def create_classifier(options, num_concepts): if options.bm25: trf = BM25Transformer(sublinear_tf=True if options.lsa else False, use_idf=options.idf, norm=norm, bm25_tf=True, use_bm25idf=True) - else: + elif options.terms or options.concepts: trf = TfidfTransformer(sublinear_tf=True if options.lsa else False, use_idf=options.idf, norm=norm) # Pipeline with final estimator ## @@ -416,9 +426,10 @@ def create_classifier(options, num_concepts): # svd = TruncatedSVD(n_components=options.lsa) # lsa = make_pipeline(svd, Normalizer(copy=False)) # clf = Pipeline([("trf", trf), ("lsa", lsa), ("clf", classifiers[options.clf_key])]) - else: + elif options.terms or options.concepts: clf = Pipeline([("trf", trf), ("clf", classifiers[options.clf_key])]) - + else: + clf = classifiers[options.clf_key] return clf def _generate_parsers(): @@ -495,6 +506,8 @@ def _generate_parsers(): "use concepts [False]") feature_options.add_argument('-t', '--terms', action="store_true", dest="terms", default=False, help= \ "use terms [True]") + feature_options.add_argument('--onehot', action="store_true", dest="onehot", default=False, help= \ + "Encode the input words as one hot. [True]") feature_options.add_argument('--max_features', type=int, dest="max_features", default=None, help= \ "Specify the maximal number of features to be considered for a BoW model [None, i.e., infinity]") feature_options.add_argument('-s', '--synsets', action="store_true", dest="synsets", default=False, help= \ @@ -520,7 +533,7 @@ def _generate_parsers(): classifier_options.add_argument('-f', '--classifier', dest="clf_key", default="nn", help= "Specify the final classifier.", choices=["nn", "brknna", "brknnb", "bbayes", "mbayes", "lsvc", "sgd", "sgddt", "rocchio", "rocchiodt", "logregress", "logregressdt", - "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase', "mlpsoph"]) + "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase', "mlpsoph", "cnn"]) classifier_options.add_argument('-a', '--alpha', dest="alpha", type=float, default=1e-7, help= \ "Specify alpha parameter for stochastic gradient descent") classifier_options.add_argument('-n', dest="k", type=int, default=1, help= From 932df353b2fc242238d83ae33233679a6517a7a6 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 29 Jun 2017 19:30:21 +0200 Subject: [PATCH 036/116] One-Hot encoding of text --- Code/lucid_ml/utils/text_encoding.py | 78 ++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 Code/lucid_ml/utils/text_encoding.py diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py new file mode 100644 index 0000000..f2a47b4 --- /dev/null +++ b/Code/lucid_ml/utils/text_encoding.py @@ -0,0 +1,78 @@ +from sklearn.base import BaseEstimator, TransformerMixin + +from utils.nltk_normalization import NltkNormalizer + +import numpy as np + + + + +class TextEncoder(BaseEstimator, TransformerMixin): + + def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content"): + + self.tokenize = tokenize + self.input = input_format + + def _maybe_load_text(self, text): + if self.input == "filename": + with open(text, 'r') as text_file: + text = text_file.read() + + return text + + def fit(self, X, y = None): + """ + X is expected to be a list of either strings, which represent the text directly, or paths to the + file where to find the text. + """ + + mapping = {} + max_index = 1 + max_length = 0 + for text in X: + + # if full-text: load it first + text = self._maybe_load_text(text) + + + + # tokenize training text + + words = self.tokenize(text) + + # build mapping from word to index + + for word in words: + if word not in mapping: + mapping[word] = max_index + max_index += 1 + + # determine maximum length of a text for padding + if len(words) > max_length: + max_length = len(words) + + + # save variables required for transformation step + self.mapping = mapping + self.max_index = max_index - 1 + self.max_length = max_length + + return self + + def transform(self, X, y = None): + + encoding_matrix = np.zeros((len(X), self.max_length), dtype = np.int32) + for i, text in enumerate(X): + + text = self._maybe_load_text(text) + + # tokenize test text + words = self.tokenize(text) + + # apply mapping from word to integer + id_sequence = np.array([self.mapping[word] for word in words if word in self.mapping]) + + encoding_matrix[i, :len(id_sequence)] = id_sequence + + return encoding_matrix \ No newline at end of file From 02b53235a61f01210b8a3a4c27b4c4fb0df2ce81 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 30 Jun 2017 07:39:33 +0200 Subject: [PATCH 037/116] Fixed a crash occuring when using onehot encoding with validation set --- Code/lucid_ml/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 9cf825b..c42ef8f 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -429,7 +429,7 @@ def create_classifier(options, num_concepts): elif options.terms or options.concepts: clf = Pipeline([("trf", trf), ("clf", classifiers[options.clf_key])]) else: - clf = classifiers[options.clf_key] + clf = Pipeline([("clf", classifiers[options.clf_key])]) return clf def _generate_parsers(): From 2f77e0676507b7709e571950a4ce966722bdd257 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sat, 1 Jul 2017 09:09:14 +0200 Subject: [PATCH 038/116] Option for limiting number of words to be taken into account when using TextEncoder via max_features --- Code/lucid_ml/run.py | 17 +++++++++-------- Code/lucid_ml/utils/text_encoding.py | 12 +++++++++++- 2 files changed, 20 insertions(+), 9 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 9cf825b..ac3f6bf 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -131,7 +131,7 @@ def run(options): elif options.concepts: extractor = concepts elif options.onehot: - extractor = TextEncoder(input_format = "filename" if options.fulltext else "content") + extractor = TextEncoder(input_format = "filename" if options.fulltext else "content", max_words=options.max_features) else: raise ValueError("No feature representation specified!") @@ -142,14 +142,15 @@ def run(options): if options.persist: persister.persist(X, Y, tr) - if VERBOSE > 3: - print("X = " + repr(X)) - print("Vocabulary size: {}".format(X.shape[1])) + if VERBOSE: + print("Feature size: {}".format(X.shape[1])) print("Number of documents: {}".format(X.shape[0])) - print("Mean distinct words per document: {}".format(X.count_nonzero() / - X.shape[0])) - words = X.sum(axis=1) - print("Mean word count per document: {} ({})".format(words.mean(), words.std())) + # these printouts only make sense if we have BoW representation + if sps.issparse(X): + print("Mean distinct words per document: {}".format(X.count_nonzero() / + X.shape[0])) + words = X.sum(axis=1) + print("Mean word count per document: {} ({})".format(words.mean(), words.std())) if VERBOSE > 1: X_tmp = X.todense() diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index f2a47b4..5b1400c 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -9,10 +9,11 @@ class TextEncoder(BaseEstimator, TransformerMixin): - def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content"): + def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None): self.tokenize = tokenize self.input = input_format + self.max_words = max_words def _maybe_load_text(self, text): if self.input == "filename": @@ -21,6 +22,12 @@ def _maybe_load_text(self, text): return text + def _limit_num_words(self, words, max_length): + if self.max_words is not None: + return words[:max_length] + else: + return words + def fit(self, X, y = None): """ X is expected to be a list of either strings, which represent the text directly, or paths to the @@ -40,6 +47,7 @@ def fit(self, X, y = None): # tokenize training text words = self.tokenize(text) + words = self._limit_num_words(words, self.max_words) # build mapping from word to index @@ -69,6 +77,8 @@ def transform(self, X, y = None): # tokenize test text words = self.tokenize(text) + # make sure we do not exceed the maximum length from training samples + words = self._limit_num_words(words, self.max_length) # apply mapping from word to integer id_sequence = np.array([self.mapping[word] for word in words if word in self.mapping]) From 43c80937d98f6c84ffa6abcbd98a7f3f7038ebae Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 2 Jul 2017 12:35:12 +0200 Subject: [PATCH 039/116] Fixed an error with type of feature matrix --- Code/lucid_ml/classifying/tensorflow_models.py | 5 +++-- Code/lucid_ml/run.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 7a24693..65f88b5 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -1,6 +1,7 @@ import numpy as np import tensorflow as tf from scipy.sparse.csr import csr_matrix +import scipy.sparse as sps from sklearn.base import BaseEstimator import math, numbers from tensorflow.python.framework import ops, tensor_shape, tensor_util @@ -184,8 +185,8 @@ def _batch_generator(self): batch_index = self.sample_index[self.batch_size * self.counter:self.batch_size * (self.counter + 1)] X_batch = self.X[batch_index, :] - if type(X_batch) != np.ndarray: - X_batch.toarray() + if sps.issparse(X_batch): + X_batch = X_batch.toarray() if not self.predict: y_batch = self.y[batch_index].toarray() diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index cdc9094..7658c69 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -30,6 +30,7 @@ from sklearn.pipeline import FeatureUnion, make_pipeline, Pipeline from sklearn.preprocessing import MultiLabelBinarizer from sklearn.svm import LinearSVC +import scipy.sparse as sps import tensorflow as tf @@ -50,7 +51,6 @@ from weighting.concept_analysis import ConceptAnalyzer from weighting.graph_score_vectorizer import GraphVectorizer from utils.text_encoding import TextEncoder -import scipy.sparse as sps logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') From 05d584ace022e8fb2afc19d3329d72b114a42f7c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 2 Jul 2017 13:10:31 +0200 Subject: [PATCH 040/116] Save model of best validation store to distinct path based on current time to be able to run multiple experiments concurrently --- Code/lucid_ml/classifying/tensorflow_models.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 65f88b5..21253e0 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -3,11 +3,11 @@ from scipy.sparse.csr import csr_matrix import scipy.sparse as sps from sklearn.base import BaseEstimator -import math, numbers +import math, numbers, os from tensorflow.python.framework import ops, tensor_shape, tensor_util from tensorflow.python.ops import math_ops, random_ops, array_ops from tensorflow.python.layers import utils - +from datetime import datetime #tf.logging.set_verbosity(tf.logging.INFO) def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): @@ -259,7 +259,13 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), t self.learning_rate = learning_rate # path to save the tensorflow model to - self._save_model_path = './best-model' + self._save_model_path = self._get_save_model_path() + + def _get_save_model_path(self): + TMP_FOLDER = ".tmp_best_models" + if not os.path.exists(TMP_FOLDER): + os.makedirs(TMP_FOLDER) + return TMP_FOLDER + "/best-model-" + self.get_model.__name__ + str(datetime.now()) def _calc_num_steps(self, X): return int(np.ceil(X.shape[0] / self.batch_size)) From 146b20309ea9c130115496ff5fcee95ee2c8edc2 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 2 Jul 2017 20:46:25 +0200 Subject: [PATCH 041/116] Implemented simple multi-layer LSTM. --- .../lucid_ml/classifying/tensorflow_models.py | 74 +++++++++++-------- Code/lucid_ml/run.py | 11 ++- 2 files changed, 50 insertions(+), 35 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 21253e0..1355832 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -10,6 +10,40 @@ from datetime import datetime #tf.logging.set_verbosity(tf.logging.INFO) +def _embeddings(x_tensor, vocab_size, embedding_size): + with tf.device('/cpu:0'), tf.name_scope("embedding"): + lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), + name="W") + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) + return embedded_words + +def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): + """Model function for LSTM.""" + + # set vocab size to the largest word identifier that exists in the training data + 1 + vocab_size = int(np.max(X)) + 1 + sequence_length = X.shape[1] + x_tensor = tf.placeholder(tf.int32, shape=(None, sequence_length), name = "x") + y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") + dropout_tensor = tf.placeholder(tf.float32, name = "dropout") + + params_fit = {dropout_tensor : 1 - keep_prob_dropout} + params_predict = {dropout_tensor : 1} + + embedded_words = _embeddings(x_tensor, vocab_size, embedding_size) + + # build multiple layers of lstms + stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) + state = stacked_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) + #state = tf.zeros([tf.shape(embedded_words)[0].value, stacked_lstm.state_size]) + + # we can discard the state after the batch is fully processed + embedded_words = tf.reshape(embedded_words, [-1, embedding_size * sequence_length]) + output_state, _ = stacked_lstm(embedded_words, state) + + hidden_layer = tf.nn.dropout(output_state, dropout_tensor) + + return x_tensor, y_tensor, hidden_layer, params_fit, params_predict def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): """Model function for CNN.""" @@ -23,14 +57,10 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - with tf.device('/cpu:0'), tf.name_scope("embedding"): - lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), - name="W") - embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) - - # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling - embedded_words = tf.expand_dims(embedded_words, -1) + embedded_words = _embeddings(x_tensor, vocab_size, embedding_size) + # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling + embedded_words = tf.expand_dims(embedded_words, -1) # these are set according to Kim's Sentence Classification window_sizes = [3, 4, 5] num_filters = 100 @@ -58,6 +88,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) return x_tensor, y_tensor, hidden_layer, params_fit, params_predict + def mlp_base_fn(X, y, keep_prob_dropout = 0.5): """Model function for MLP-Soph.""" # convert sparse tensors to dense @@ -163,6 +194,10 @@ def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing) def cnn(keep_prob_dropout, embedding_size, hidden_layers): return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers) + +def lstm(keep_prob_dropout, embedding_size, hidden_layers): + return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, + hidden_layers = hidden_layers) class BatchGenerator: @@ -200,31 +235,6 @@ def _batch_generator(self): else: return X_batch - - -#=============================================================================== -# def lstm(lr, dropout): -# if lr is None: -# # set lr to default option -# lr = 0.1 -# return lambda : (cnn_fn, {"learning_rate": lr, "dropout" : dropout}) -#=============================================================================== - -#=============================================================================== -# def _sparse_to_sparse(X): -# non_zero_rows, non_zero_cols = X.nonzero() -# indices = np.array([[r, c] for r, c in zip(non_zero_rows, non_zero_cols)]) -# values = np.array([X[r,c] for r, c in zip(non_zero_rows, non_zero_cols)]) -# -# shape = list(X.shape) -# input_tensor = tf.SparseTensor(indices=indices, -# values=values, -# dense_shape=shape) -# return input_tensor -#=============================================================================== - - - class MultiLabelSKFlow(BaseEstimator): """ This is a wrapper class for tf.contrib.learn.Estimators, so it adheres to the fit/predict naming conventions of sk-learn. diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 7658c69..90aa0e1 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -40,7 +40,7 @@ from classifying.nearest_neighbor import NearestNeighbor from classifying.rocchioclassifier import RocchioClassifier from classifying.stacked_classifier import ClassifierStack -from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base, mlp_soph, cnn +from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base, mlp_soph, cnn, lstm from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample from utils.nltk_normalization import NltkNormalizer, word_regexp @@ -271,7 +271,7 @@ def split(self, X): Y_train = np.vstack((Y_train, Y_val)) # mlp doesn't seem to like being stuck into a new process... - if options.debug or options.clf_key in {'mlp', 'mlpthr', 'mlpsoph', "cnn", "mlpbase"}: + if options.debug or options.clf_key in {'mlp', 'mlpthr', 'mlpsoph', "cnn", "mlpbase", "lstm"}: Y_pred, Y_train_pred = fit_predict(X_test, X_train, Y_train, options, tr, clf) else: Y_pred, Y_train_pred = fit_predict_new_process(X_test, X_train, Y_train, options, tr, clf) @@ -404,6 +404,11 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers)), + "lstm": MultiLabelSKFlow(batch_size = options.batch_size, + num_epochs=options.max_iterations, + learning_rate = options.learning_rate, + get_model = lstm(options.dropout, options.embedding_size, + hidden_layers = options.hidden_layers)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -534,7 +539,7 @@ def _generate_parsers(): classifier_options.add_argument('-f', '--classifier', dest="clf_key", default="nn", help= "Specify the final classifier.", choices=["nn", "brknna", "brknnb", "bbayes", "mbayes", "lsvc", "sgd", "sgddt", "rocchio", "rocchiodt", "logregress", "logregressdt", - "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase', "mlpsoph", "cnn"]) + "mlp", "listnet", "l2rdt", 'mlpthr', 'mlpdt', 'nam', 'mlpbase', "mlpsoph", "cnn", "lstm"]) classifier_options.add_argument('-a', '--alpha', dest="alpha", type=float, default=1e-7, help= \ "Specify alpha parameter for stochastic gradient descent") classifier_options.add_argument('-n', dest="k", type=int, default=1, help= From 2c5afda66d71ae8c5ee96526d9fda37a8fe666c6 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 3 Jul 2017 12:01:17 +0200 Subject: [PATCH 042/116] Use dynamic_rnn as a wrapper instead of multi-rnn-cell --- Code/lucid_ml/classifying/tensorflow_models.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 1355832..c17261b 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -17,7 +17,7 @@ def _embeddings(x_tensor, vocab_size, embedding_size): embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) return embedded_words -def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): +def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True): """Model function for LSTM.""" # set vocab size to the largest word identifier that exists in the training data + 1 @@ -38,8 +38,10 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = #state = tf.zeros([tf.shape(embedded_words)[0].value, stacked_lstm.state_size]) # we can discard the state after the batch is fully processed - embedded_words = tf.reshape(embedded_words, [-1, embedding_size * sequence_length]) - output_state, _ = stacked_lstm(embedded_words, state) + output_state, _ = tf.nn.dynamic_rnn(stacked_lstm, embedded_words, initial_state = state) + + if aggregate_output: + output_state = tf.reduce_mean(output_state, axis = 1) hidden_layer = tf.nn.dropout(output_state, dropout_tensor) From 3bc12ed4d53f6b15bbe4868c609211c02a90c8ef Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 3 Jul 2017 13:22:14 +0200 Subject: [PATCH 043/116] Encode in feature matrix and extract from it the size of vocabulary --- .../lucid_ml/classifying/tensorflow_models.py | 32 ++++++++++++------- Code/lucid_ml/utils/text_encoding.py | 4 ++- 2 files changed, 23 insertions(+), 13 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index c17261b..b328b08 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -17,20 +17,28 @@ def _embeddings(x_tensor, vocab_size, embedding_size): embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) return embedded_words +def _extract_vocab_size(X): + # max index of vocabulary is encoded in last column + vocab_size = X[0, -1] + 1 + + # slice of the column from the input, as it's not part of the sequence + sequence_length = X.shape[1] + x_tensor = tf.placeholder(tf.int32, shape=(None, sequence_length), name = "x") + feature_input = tf.slice(x_tensor, [0, 0], [-1, sequence_length - 1]) + return x_tensor, vocab_size, feature_input + def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True): """Model function for LSTM.""" - # set vocab size to the largest word identifier that exists in the training data + 1 - vocab_size = int(np.max(X)) + 1 - sequence_length = X.shape[1] - x_tensor = tf.placeholder(tf.int32, shape=(None, sequence_length), name = "x") + x_tensor, vocab_size, feature_input = _extract_vocab_size(X) + y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words = _embeddings(x_tensor, vocab_size, embedding_size) + embedded_words = _embeddings(feature_input, vocab_size, embedding_size) # build multiple layers of lstms stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) @@ -48,18 +56,18 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = return x_tensor, y_tensor, hidden_layer, params_fit, params_predict def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): """Model function for CNN.""" - - # set vocab size to the largest word identifier that exists in the training data + 1 - vocab_size = int(np.max(X)) + 1 - sequence_length = X.shape[1] - x_tensor = tf.placeholder(tf.int32, shape=(None, sequence_length), name = "x") + + # x_tensor includes the max_index_column, feature_input doesnt. go on with feature_input, but return x_tensor for feed_dict + x_tensor, vocab_size, feature_input = _extract_vocab_size(X) + sequence_length = X.shape[1] - 1 + y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words = _embeddings(x_tensor, vocab_size, embedding_size) + embedded_words = _embeddings(feature_input, vocab_size, embedding_size) # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling embedded_words = tf.expand_dims(embedded_words, -1) @@ -250,7 +258,7 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.1, learning_rate = 0.1, tolerance = 5): """ """ diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index 5b1400c..d6f4181 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -85,4 +85,6 @@ def transform(self, X, y = None): encoding_matrix[i, :len(id_sequence)] = id_sequence - return encoding_matrix \ No newline at end of file + max_index_column = np.zeros((len(X), 1), dtype = np.int32) + max_index_column.fill(self.max_index) + return np.hstack((encoding_matrix, max_index_column)) From 586f30be72e6d70d136d7f42307936576c15534f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 3 Jul 2017 14:17:54 +0200 Subject: [PATCH 044/116] May use any sklearn.metrics metric as validation score now, f1-samples by default --- .../lucid_ml/classifying/tensorflow_models.py | 42 +++++++++++++------ 1 file changed, 29 insertions(+), 13 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index b328b08..7e75a8a 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -3,6 +3,7 @@ from scipy.sparse.csr import csr_matrix import scipy.sparse as sps from sklearn.base import BaseEstimator +from sklearn.metrics import f1_score import math, numbers, os from tensorflow.python.framework import ops, tensor_shape, tensor_util from tensorflow.python.ops import math_ops, random_ops, array_ops @@ -258,7 +259,8 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.1, learning_rate = 0.1, tolerance = 5): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5, + validation_metric = lambda y1, y2 : f1_score(y1, y2, average = "samples")): """ """ @@ -269,6 +271,7 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), t self.validation_data_position = None # used by this class + self.validation_metric = validation_metric self.tolerance = tolerance self.batch_size = batch_size self.num_epochs = num_epochs @@ -289,7 +292,20 @@ def _get_save_model_path(self): def _calc_num_steps(self, X): return int(np.ceil(X.shape[0] / self.batch_size)) - + + def _compute_validation_score(self, session, X_val_batch, y_val_batch): + + feed_dict = {self.x_tensor: X_val_batch, self.y_tensor: y_val_batch} + feed_dict.update(self.params_predict) + + if self.validation_metric == "val_loss": + return session.run(self.loss, feed_dict = feed_dict) + + elif callable(self.validation_metric): + predictions = session.run(self.predictions, feed_dict = feed_dict) + y_pred = csr_matrix(predictions > self.threshold) + return self.validation_metric(y_val_batch, y_pred) + def fit(self, X, y): self.y = y @@ -334,8 +350,9 @@ def fit(self, X, y): batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) # Training cycle - avg_validation_loss = math.inf - best_validation_loss = math.inf + objective = 1 if self.validation_metric == "val_loss" else -1 + avg_validation_score = math.inf * objective + best_validation_score = math.inf * objective epochs_of_no_improvement = 0 for epoch in range(self.num_epochs): @@ -355,20 +372,19 @@ def fit(self, X, y): # calculate validation loss at end of epoch if early stopping is on if batch_i + 1 == steps_per_epoch and val_pos is not None: - validation_losses = [] + validation_scores = [] weights = [] for _ in range(validation_predictions): X_val_batch, y_val_batch = validation_batch_generator._batch_generator() weights.append(X_val_batch.shape[0]) - feed_dict = {self.x_tensor: X_val_batch, self.y_tensor: y_val_batch} - feed_dict.update(self.params_predict) - validation_losses.append(session.run(self.loss, feed_dict = feed_dict)) - avg_validation_loss = np.average(np.array(validation_losses), weights = np.array(weights)) + validation_scores.append(self._compute_validation_score(session, X_val_batch, y_val_batch)) + avg_validation_score = np.average(np.array(validation_scores), weights = np.array(weights)) - if avg_validation_loss < best_validation_loss: + is_better_score = avg_validation_score < best_validation_score if objective == 1 else avg_validation_score > best_validation_score + if is_better_score: # save model # Save model for prediction step - best_validation_loss = avg_validation_loss + best_validation_score = avg_validation_score saver = tf.train.Saver() saver.save(session, self._save_model_path) epochs_of_no_improvement = 0 @@ -379,9 +395,9 @@ def fit(self, X, y): break # print progress - print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Loss: {:0.4f}, Best Validation-Loss: {:0.4f}'.format(epoch + 1, self.num_epochs, + print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Score: {:0.4f}, Best Validation-Score: {:0.4f}'.format(epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, - loss, avg_validation_loss, best_validation_loss), end='\r') + loss, avg_validation_score, best_validation_score), end='\r') self.session = session print('') From 5768e8b93a224d9cdeb87130b5b9ec1ab25c5552 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 3 Jul 2017 16:06:22 +0200 Subject: [PATCH 045/116] Use --optimize_threshold to optimize threshold on validation set during training --- .../lucid_ml/classifying/tensorflow_models.py | 44 +++++++++++++++---- Code/lucid_ml/run.py | 6 +++ 2 files changed, 42 insertions(+), 8 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 7e75a8a..d509805 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -260,7 +260,9 @@ class MultiLabelSKFlow(BaseEstimator): """ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5, - validation_metric = lambda y1, y2 : f1_score(y1, y2, average = "samples")): + validation_metric = lambda y1, y2 : f1_score(y1, y2, average = "samples"), + optimize_threshold = True, + threshold_window = np.linspace(-0.03, 0.03, num=7)): """ """ @@ -272,6 +274,8 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), t # used by this class self.validation_metric = validation_metric + self.optimize_threshold = optimize_threshold + self.threshold_window = threshold_window self.tolerance = tolerance self.batch_size = batch_size self.num_epochs = num_epochs @@ -304,7 +308,10 @@ def _compute_validation_score(self, session, X_val_batch, y_val_batch): elif callable(self.validation_metric): predictions = session.run(self.predictions, feed_dict = feed_dict) y_pred = csr_matrix(predictions > self.threshold) - return self.validation_metric(y_val_batch, y_pred) + if self.optimize_threshold: + return self.validation_metric(y_val_batch, y_pred), predictions + else: + return self.validation_metric(y_val_batch, y_pred) def fit(self, X, y): self.y = y @@ -314,7 +321,7 @@ def fit(self, X, y): if val_pos is not None: X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:, :] - validation_batch_generator = BatchGenerator(X_val, y_val, X_val.shape[0], False, False) + validation_batch_generator = BatchGenerator(X_val, y_val, self.batch_size, False, False) validation_predictions = self._calc_num_steps(X_val) steps_per_epoch = self._calc_num_steps(X_train) else: @@ -374,12 +381,30 @@ def fit(self, X, y): if batch_i + 1 == steps_per_epoch and val_pos is not None: validation_scores = [] weights = [] - for _ in range(validation_predictions): + + # save predictions so we can optimize threshold later + val_predictions = np.zeros((X_val.shape[0], self.y.shape[1])) + for i in range(validation_predictions): X_val_batch, y_val_batch = validation_batch_generator._batch_generator() weights.append(X_val_batch.shape[0]) - validation_scores.append(self._compute_validation_score(session, X_val_batch, y_val_batch)) + + if self.optimize_threshold: + batch_val_score, val_predictions[i * self.batch_size:(i+1) * self.batch_size, :] = self._compute_validation_score(session, X_val_batch, y_val_batch) + else: + batch_val_score = self._compute_validation_score(session, X_val_batch, y_val_batch) + validation_scores.append(batch_val_score) avg_validation_score = np.average(np.array(validation_scores), weights = np.array(weights)) + if self.optimize_threshold: + best_score = -1 * math.inf + best_threshold = self.threshold + for t_diff in self.threshold_window: + t = self.threshold + t_diff + score = self.validation_metric(y_val, csr_matrix(val_predictions > t)) + if score > best_score: + best_threshold = t + best_score = score + is_better_score = avg_validation_score < best_validation_score if objective == 1 else avg_validation_score > best_validation_score if is_better_score: # save model @@ -388,6 +413,10 @@ def fit(self, X, y): saver = tf.train.Saver() saver.save(session, self._save_model_path) epochs_of_no_improvement = 0 + + # save the threshold at best model, too. + if self.optimize_threshold: + self.threshold = best_threshold else: epochs_of_no_improvement += 1 if epochs_of_no_improvement > self.tolerance: @@ -395,9 +424,8 @@ def fit(self, X, y): break # print progress - print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Score: {:0.4f}, Best Validation-Score: {:0.4f}'.format(epoch + 1, self.num_epochs, - batch_i + 1, steps_per_epoch, - loss, avg_validation_score, best_validation_score), end='\r') + print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Score: {:0.4f}, Best Validation-Score: {:0.4f}, Threshold: {:0.2f}'. + format(epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, loss, avg_validation_score, best_validation_score, self.threshold), end='\r') self.session = session print('') diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 90aa0e1..8786854 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -393,20 +393,24 @@ def create_classifier(options, num_concepts): "mlpbase" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + optimize_threshold = options.optimize_threshold, get_model = mlp_base(options.dropout)), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + optimize_threshold = options.optimize_threshold, get_model = mlp_soph(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, self_normalizing = options.snn)), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + optimize_threshold = options.optimize_threshold, get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers)), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + optimize_threshold = options.optimize_threshold, get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, @@ -583,6 +587,8 @@ def _generate_parsers(): specifies the number of units in layer i. [1000]") neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= "Whether to use SELU activation and -dropout. If set to False, RELU activation is used. [False]") + neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= + "Optimize the prediction threshold on validation set during training. [False]") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 850b9e637653417f58069d2528106a92c7d82cbb Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 3 Jul 2017 16:14:59 +0200 Subject: [PATCH 046/116] Specify folder to save weights of best model to --- Code/lucid_ml/classifying/tensorflow_models.py | 6 ++++-- Code/lucid_ml/run.py | 6 ++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 1355832..6b7cc3a 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -248,7 +248,8 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5): + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5, + tf_model_path = ".tmp_best_models"): """ """ @@ -269,10 +270,11 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), t self.learning_rate = learning_rate # path to save the tensorflow model to + self.TF_MODEL_PATH = ".tmp_best_models" self._save_model_path = self._get_save_model_path() def _get_save_model_path(self): - TMP_FOLDER = ".tmp_best_models" + TMP_FOLDER = self.TF_MODEL_PATH if not os.path.exists(TMP_FOLDER): os.makedirs(TMP_FOLDER) return TMP_FOLDER + "/best-model-" + self.get_model.__name__ + str(datetime.now()) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 90aa0e1..5a62202 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -393,20 +393,24 @@ def create_classifier(options, num_concepts): "mlpbase" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + tf_model_path = options.tf_model_path, get_model = mlp_base(options.dropout)), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + tf_model_path = options.tf_model_path, get_model = mlp_soph(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, self_normalizing = options.snn)), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + tf_model_path = options.tf_model_path, get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers)), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, + tf_model_path = options.tf_model_path, get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, @@ -592,6 +596,8 @@ def _generate_parsers(): "Persisted features will be recalculated and overwritten.") persistence_options.add_argument('--persist_to', dest="persist_to", default=os.curdir + os.sep + 'persistence', help= "Path to persist files.") + persistence_options.add_argument("--tf-model-path", dest="tf_model_path", default=".tmp_best_models", help= + "Directory to store best models for early stopping.") return meta_parser, parser From f96fe46f47ba3e3e553fb2b6da07209e9c4ac40a Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 23 Jul 2017 13:13:19 +0200 Subject: [PATCH 047/116] Little bit of refactorization in run.py script --- Code/lucid_ml/run.py | 45 +++++++++++++++++++++++++++++++++++--------- 1 file changed, 36 insertions(+), 9 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 3468edd..5db6143 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -55,7 +55,7 @@ logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') -def run(options): +def _build_features(options): DATA_PATHS = json.load(options.key_file) VERBOSE = options.verbose persister = Persister(DATA_PATHS, options) @@ -64,6 +64,7 @@ def run(options): if VERBOSE: print("Y = " + str(Y.shape)) else: # --- LOAD DATA --- + fold_list = None if options.fixed_folds: X_raw, Y_raw, tr, fold_list = load_dataset(DATA_PATHS, options.data_key, options.fulltext, fixed_folds=True) else: @@ -141,7 +142,11 @@ def run(options): if VERBOSE > 1: print(default_timer() - start_ef) if options.persist: persister.persist(X, Y, tr) + + return X, Y, extractor, mlb, fold_list, X_raw, Y_raw, tr +def _print_feature_info(X, options): + VERBOSE = options.verbose if VERBOSE: print("Feature size: {}".format(X.shape[1])) print("Number of documents: {}".format(X.shape[0])) @@ -170,7 +175,7 @@ def run(options): # n_iter = np.ceil(10**6 / (X.shape[0] * 0.9)) # print("Dynamic n_iter = %d" % n_iter) - +def _check_interactive(options, X, Y, extractor, mlb): if options.interactive: print("Please wait...") clf = create_classifier(options, Y.shape[1]) # --- INTERACTIVE MODE --- @@ -188,13 +193,9 @@ def run(options): exit(1) exit(0) - if VERBOSE: print("Performing %d-fold cross-validation..." % (options.folds if options.cross_validation else 1)) - - if options.plot: - all_f1s = [] - - # --- CROSS-VALIDATION --- - scores = defaultdict(list) +def _build_folds(options, fold_list): + validation_set_indices = None + if options.cross_validation: kf = KFold(n_splits=options.folds, shuffle=True) elif options.fixed_folds: @@ -235,6 +236,15 @@ def split(self, X): else: kf = ShuffleSplit(test_size=options.test_size, n_splits = 1) + + return kf, validation_set_indices + +def _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, options): + VERBOSE = options.verbose + scores = defaultdict(list) + if options.plot: + all_f1s = [] + for iteration, (train, test) in enumerate(kf.split(X)): if VERBOSE: print("=" * 80) @@ -329,6 +339,23 @@ def split(self, X): return results +def run(options): + VERBOSE = options.verbose + + # load dataset and build feature representation + X, Y, extractor, mlb, fold_list, X_raw, Y_raw, tr = _build_features(options) + _print_feature_info(X, options) + + # go to interactive mode if on + _check_interactive(options, X, Y, extractor, mlb) + + if VERBOSE: print("Performing %d-fold cross-validation..." % (options.folds if options.cross_validation else 1)) + + # prepare validation over folds + kf, validation_set_indices = _build_folds(options, fold_list) + + results = _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, options) + def fit_predict(X_test, X_train, Y_train, options, tr, clf): if options.verbose: print("Fitting", X_train.shape[0], "samples...") clf.fit(X_train, Y_train) From 41e20fab6e3dc5113b91e963fd7ef0edcc64f0e5 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 23 Jul 2017 18:31:50 +0200 Subject: [PATCH 048/116] Random search and bayesian hyperparameter optimization --- Code/lucid_ml/run.py | 84 ++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 82 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 5db6143..6889e29 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -32,7 +32,8 @@ from sklearn.svm import LinearSVC import scipy.sparse as sps -import tensorflow as tf +from bayes_opt import BayesianOptimization +from hyperopt import fmin, rand, hp from classifying.br_kneighbor_classifier import BRKNeighborsClassifier from classifying.kneighbour_l2r_classifier import KNeighborsL2RClassifier @@ -338,7 +339,55 @@ def _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, opt return results +def _update_options(options, **parameters): + for param_name, param_value in parameters.items(): + print("I'm a param:", param_name, "with value", param_value) + + try: + setattr(options, param_name, param_value) + except AttributeError: + print("Can't find parameter ", param_name, "so we'll not use it.") + continue + + return options + +def _make_space(options): + + space = {} + inits = {} + with open(options.optimization_spaces) as optimization_file: + for line in optimization_file: + + # escape comments + if line.startswith("#"): + continue + + line = line.strip() + info = line.split(",") + param_name = info[0] + left_bound, right_bound = float(info[1]), float(info[2]) + + if options.optimization == "random": + param_type = info[3] + + try: + param_type = getattr(hp, param_type) + except AttributeError: + print("hyperopt has no attribute", param_type) + continue + + space[param_name] = param_type(param_name, left_bound, right_bound) + elif options.optimization == "bayesian": + init_val = float(info[3]) + inits[param_name] = [init_val] + space[param_name] = (left_bound, right_bound) + + if options.optimization == "bayesian": + return space, inits + else: + return space + def run(options): VERBOSE = options.verbose @@ -353,8 +402,33 @@ def run(options): # prepare validation over folds kf, validation_set_indices = _build_folds(options, fold_list) + + if options.optimization: + + def optimized_experiment(**parameters): + + current_options = _update_options(options, **parameters) + results = _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, current_options) - results = _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, options) + # return the f1 score of the previous experiment + return results["f1_samples"][0] + + if options.optimization == "bayesian": + + gp_params = {"alpha": 1e-5} + space, init_vals = _make_space(options) + bayesian_optimizer = BayesianOptimization(optimized_experiment, space) + bayesian_optimizer.explore(init_vals) + bayesian_optimizer.maximize(n_iter=options.optimization_iterations, **gp_params) + + elif options.optimization == "random": + + fmin(lambda parameters : optimized_experiment(**parameters), + _make_space(options), + algo=rand.suggest, + max_evals=options.optimization_iterations) + else: + results = _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, options) def fit_predict(X_test, X_train, Y_train, options, tr, clf): if options.verbose: print("Fitting", X_train.shape[0], "samples...") @@ -519,6 +593,12 @@ def _generate_parsers(): detailed_options = parser.add_argument_group("Detailed Execution Options") detailed_options.add_argument('--test-size', type=float, dest='test_size', default=0.1, help= "Desired relative size for the test set [0.1]") + detailed_options.add_argument('--optimization', type=str, dest='optimization', default=None, help= + "Whether to use Random Search or Bayesian Optimization for hyperparameter search. [None]", choices = ["random", "bayesian", None]) + detailed_options.add_argument('--optimization_spaces', type=str, dest='optimization_spaces', default="default_searchspace", help= + "Path to a file that specifies the search spaces for hyperparameters [default_searchspace]") + detailed_options.add_argument('--optimization_iterations', type=int, dest='optimization_iterations', default=10, help= + "Number of iterations in hyperparameter search. [10]") detailed_options.add_argument('--val-size', type=float, dest='validation_size', default=0., help= "Desired relative size of the training set used as validation set [0.]") detailed_options.add_argument('--folds', type=int, dest='folds', default=10, help= From 6ea18fc45c5d6c2a0fc6f75b6ebb8c749791ad2f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 26 Jul 2017 14:35:37 +0200 Subject: [PATCH 049/116] Default search strategies adapted to Snoerk --- Code/lucid_ml/default_searchspace | 17 +++++++++++++++++ Code/lucid_ml/run.py | 8 ++++++-- 2 files changed, 23 insertions(+), 2 deletions(-) create mode 100644 Code/lucid_ml/default_searchspace diff --git a/Code/lucid_ml/default_searchspace b/Code/lucid_ml/default_searchspace new file mode 100644 index 0000000..659da1c --- /dev/null +++ b/Code/lucid_ml/default_searchspace @@ -0,0 +1,17 @@ +# the structure should be like this: +# +# for random search, specify the following format: +# <parameter_name_1>,<lower_bound_1>,<upper_bound_1>,<space_type_1> +# ... +# <parameter_name_n>,<lower_bound_n>,<upper_bound_n>,<space_type_n> +# example: +learning_rate,-9.21,-2.3,loguniform +dropout,0.05,0.95,uniform +# +# for bayesian optimization, specify in the following format: +# <parameter_name_1>,<lower_bound_1>,<upper_bound_1>,<init_value_1> +# ... +# <parameter_name_n>,<lower_bound_n>,<upper_bound_n>,<init_value_n> +# example: +#learning_rate,0.0001,1,0.1 +#dropout,0.05,0.95,0.05 \ No newline at end of file diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 6889e29..f8b5be0 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -32,8 +32,10 @@ from sklearn.svm import LinearSVC import scipy.sparse as sps +# imports for hyperparameter optimization from bayes_opt import BayesianOptimization from hyperopt import fmin, rand, hp +from sklearn.gaussian_process.kernels import Matern from classifying.br_kneighbor_classifier import BRKNeighborsClassifier from classifying.kneighbour_l2r_classifier import KNeighborsL2RClassifier @@ -415,11 +417,13 @@ def optimized_experiment(**parameters): if options.optimization == "bayesian": - gp_params = {"alpha": 1e-5} + gp_params = {"alpha": 1e-5, "kernel" : Matern(nu = 5 / 2)} space, init_vals = _make_space(options) bayesian_optimizer = BayesianOptimization(optimized_experiment, space) bayesian_optimizer.explore(init_vals) - bayesian_optimizer.maximize(n_iter=options.optimization_iterations, **gp_params) + bayesian_optimizer.maximize(n_iter=options.optimization_iterations - 1, + acq = 'ei', + **gp_params) elif options.optimization == "random": From 2c48f533254afd1372bfbeddc573b871fa3c154f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 27 Jul 2017 16:07:43 +0200 Subject: [PATCH 050/116] Pretrained word embeddings --- .../lucid_ml/classifying/tensorflow_models.py | 29 ++++++-- Code/lucid_ml/utils/text_encoding.py | 73 +++++++++++++------ 2 files changed, 74 insertions(+), 28 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index de12542..73d76c5 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -11,10 +11,29 @@ from datetime import datetime #tf.logging.set_verbosity(tf.logging.INFO) -def _embeddings(x_tensor, vocab_size, embedding_size): +def _load_embeddings(filename): + + embeddings = [] + with open(filename,'r') as embedding_file: + for line in embedding_file.readlines(): + row = line.strip().split(' ') + embeddings.append(row[1:]) + print('Loaded Embedding!xxx') + embedding_size = len(embeddings[0]) + embeddings = np.asarray(embeddings) + return embeddings, embedding_size + +def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = None): + with tf.device('/cpu:0'), tf.name_scope("embedding"): - lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), - name="W") + + if pretrained_embeddings is not None: + embeddings, embedding_size = _load_embeddings(pretrained_embeddings) + lookup_table = tf.constant(embeddings, name = "W") + else: + lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), + name="W") + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) return embedded_words @@ -28,7 +47,7 @@ def _extract_vocab_size(X): feature_input = tf.slice(x_tensor, [0, 0], [-1, sequence_length - 1]) return x_tensor, vocab_size, feature_input -def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True): +def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True, pretrained_embeddings = "utils/glove.6B.50d.txt"): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -39,7 +58,7 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words = _embeddings(feature_input, vocab_size, embedding_size) + embedded_words = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = pretrained_embeddings) # build multiple layers of lstms stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index d6f4181..e1859bb 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -9,11 +9,12 @@ class TextEncoder(BaseEstimator, TransformerMixin): - def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None): + def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None, pretrained = "utils/glove.6B.50d.txt"): self.tokenize = tokenize self.input = input_format self.max_words = max_words + self.pretrained = pretrained def _maybe_load_text(self, text): if self.input == "filename": @@ -28,38 +29,64 @@ def _limit_num_words(self, words, max_length): else: return words + @staticmethod + def _load_pretrained_vocabulary(filename): + mapping = {} + with open(filename,'r') as embedding_file: + for i, line in enumerate(embedding_file.readlines()): + row = line.strip().split(' ') + mapping[row[0]] = i + print('Loaded Embedding!') + return mapping + def fit(self, X, y = None): """ X is expected to be a list of either strings, which represent the text directly, or paths to the file where to find the text. """ - mapping = {} - max_index = 1 - max_length = 0 - for text in X: - - # if full-text: load it first - text = self._maybe_load_text(text) - + if self.pretrained is None: + mapping = {} + max_index = 1 + max_length = 0 + for text in X: + + # if full-text: load it first + text = self._maybe_load_text(text) + + # tokenize training text + words = self.tokenize(text) + words = self._limit_num_words(words, self.max_words) + # build mapping from word to index + + for word in words: + if word not in mapping: + mapping[word] = max_index + max_index += 1 + + # determine maximum length of a text for padding + if len(words) > max_length: + max_length = len(words) + - # tokenize training text - - words = self.tokenize(text) - words = self._limit_num_words(words, self.max_words) - - # build mapping from word to index - - for word in words: - if word not in mapping: - mapping[word] = max_index - max_index += 1 + else: - # determine maximum length of a text for padding - if len(words) > max_length: - max_length = len(words) + mapping = TextEncoder._load_pretrained_vocabulary(self.pretrained) + max_length = 0 + for text in X: + + # if full-text: load it first + text = self._maybe_load_text(text) + + # tokenize training text and look for the maximum + words = self.tokenize(text) + words = self._limit_num_words(words, self.max_words) + if len(words) > max_length: + max_length = len(words) + + max_index = len(mapping) # save variables required for transformation step self.mapping = mapping From 1bb04d7d93fdb16fc370d49b7369e5bcc5e8b456 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 7 Aug 2017 14:15:26 +0200 Subject: [PATCH 051/116] Added fixed seeds to assure reproducability of results. --- Code/lucid_ml/classifying/tensorflow_models.py | 7 ++++--- Code/lucid_ml/run.py | 9 ++++++++- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 73d76c5..33be21f 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -18,7 +18,7 @@ def _load_embeddings(filename): for line in embedding_file.readlines(): row = line.strip().split(' ') embeddings.append(row[1:]) - print('Loaded Embedding!xxx') + print('Loaded Embedding!') embedding_size = len(embeddings[0]) embeddings = np.asarray(embeddings) return embeddings, embedding_size @@ -138,7 +138,7 @@ def mlp_base_fn(X, y, keep_prob_dropout = 0.5): # https://github.com/bioinf-jku/SNNs/blob/master/selu.py def dropout_selu(x, rate, alpha= -1.7580993408473766, fixedPointMean=0.0, fixedPointVar=1.0, - noise_shape=None, seed=None, name=None, training=False): + noise_shape=None, seed=1337, name=None, training=False): """Dropout to a value with rescaling.""" def dropout_selu_impl(x, rate, alpha, noise_shape, seed, name): @@ -352,7 +352,8 @@ def fit(self, X, y): # Remove previous weights, bias, inputs, etc.. tf.reset_default_graph() - + tf.set_random_seed(1337) + # Inputs # get_model has to return a diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index f8b5be0..548731c 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -55,6 +55,7 @@ from weighting.graph_score_vectorizer import GraphVectorizer from utils.text_encoding import TextEncoder +### SET LOGGING logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') @@ -393,6 +394,11 @@ def _make_space(options): def run(options): VERBOSE = options.verbose + ### SET SEEDS FOR REPRODUCABILITY + np.random.seed(1337) + random.seed(1337) + ### + # load dataset and build feature representation X, Y, extractor, mlb, fold_list, X_raw, Y_raw, tr = _build_features(options) _print_feature_info(X, options) @@ -430,7 +436,8 @@ def optimized_experiment(**parameters): fmin(lambda parameters : optimized_experiment(**parameters), _make_space(options), algo=rand.suggest, - max_evals=options.optimization_iterations) + max_evals=options.optimization_iterations, + rstate = np.random.RandomState(1337)) else: results = _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, options) From 9c540eb84373d073c070d2fbe28e1148f399d26f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 7 Aug 2017 17:33:05 +0200 Subject: [PATCH 052/116] You may now use pretrained word embeddings with LSTM and CNN --- .../lucid_ml/classifying/tensorflow_models.py | 51 +- Code/lucid_ml/run.py | 10 +- Code/lucid_ml/utils/glove.6B.50d.txt | 50000 ++++++++++++++++ Code/lucid_ml/utils/text_encoding.py | 1 - 4 files changed, 50044 insertions(+), 18 deletions(-) create mode 100644 Code/lucid_ml/utils/glove.6B.50d.txt diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 33be21f..04c4719 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -18,24 +18,31 @@ def _load_embeddings(filename): for line in embedding_file.readlines(): row = line.strip().split(' ') embeddings.append(row[1:]) - print('Loaded Embedding!') embedding_size = len(embeddings[0]) embeddings = np.asarray(embeddings) return embeddings, embedding_size -def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = None): +def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = True): with tf.device('/cpu:0'), tf.name_scope("embedding"): - if pretrained_embeddings is not None: - embeddings, embedding_size = _load_embeddings(pretrained_embeddings) - lookup_table = tf.constant(embeddings, name = "W") + if pretrained_embeddings: + + lookup_table = tf.Variable(tf.constant(0.0, shape=[vocab_size, embedding_size]), + trainable=False, name="W") + embedding_placeholder = tf.placeholder(tf.float32, [vocab_size, embedding_size]) + embedding_init = lookup_table.assign(embedding_placeholder) else: lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name="W") + embedding_init = lookup_table - embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) - return embedded_words + embedded_words = tf.nn.embedding_lookup(embedding_init, x_tensor) + + if pretrained_embeddings: + return embedded_words, embedding_placeholder + else: + return embedded_words def _extract_vocab_size(X): # max index of vocabulary is encoded in last column @@ -47,7 +54,19 @@ def _extract_vocab_size(X): feature_input = tf.slice(x_tensor, [0, 0], [-1, sequence_length - 1]) return x_tensor, vocab_size, feature_input -def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True, pretrained_embeddings = "utils/glove.6B.50d.txt"): +def _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict): + if pretrained_embeddings_path is not None: + embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path) + + embedded_words, embedding_placeholder = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = True) + params_fit.update({embedding_placeholder : embeddings}) + params_predict.update({embedding_placeholder : embeddings}) + else: + embedded_words = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = False) + + return embedded_words, embedding_size + +def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True, pretrained_embeddings_path = None): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -58,7 +77,7 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = pretrained_embeddings) + embedded_words, _ = _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict) # build multiple layers of lstms stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) @@ -74,7 +93,7 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = hidden_layer = tf.nn.dropout(output_state, dropout_tensor) return x_tensor, y_tensor, hidden_layer, params_fit, params_predict -def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000]): +def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], pretrained_embeddings_path = None): """Model function for CNN.""" # x_tensor includes the max_index_column, feature_input doesnt. go on with feature_input, but return x_tensor for feed_dict @@ -87,7 +106,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words = _embeddings(feature_input, vocab_size, embedding_size) + embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict) # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling embedded_words = tf.expand_dims(embedded_words, -1) @@ -221,13 +240,15 @@ def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing) return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, self_normalizing = self_normalizing) -def cnn(keep_prob_dropout, embedding_size, hidden_layers): +def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path = "utils/glove.6B.50d.txt"): + return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, - hidden_layers = hidden_layers) + hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path) -def lstm(keep_prob_dropout, embedding_size, hidden_layers): +def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path = "utils/glove.6B.50d.txt"): + return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, - hidden_layers = hidden_layers) + hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 548731c..4305d44 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -521,14 +521,16 @@ def create_classifier(options, num_concepts): tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, get_model = cnn(options.dropout, options.embedding_size, - hidden_layers = options.hidden_layers)), + hidden_layers = options.hidden_layers, + pretrained_embeddings_path = options.pretrained_embeddings)), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, get_model = lstm(options.dropout, options.embedding_size, - hidden_layers = options.hidden_layers)), + hidden_layers = options.hidden_layers, + pretrained_embeddings_path = options.pretrained_embeddings)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -704,6 +706,10 @@ def _generate_parsers(): neural_network_options.add_argument('--embedding_size', type=int, dest="embedding_size", default=300, help= "Determine the size of a word embedding vector (for MLP-Soph, CNN, and LSTM if embedding is learned jointly). \ Specify --embedding_size=0 to skip the embedding layer, if applicable. [300]") + neural_network_options.add_argument('--pretrained_embeddings', type=str, dest="pretrained_embeddings", default=None, help= + "Specify the path to a file contraining pretrained word embeddings. The file must have a format where each line consists of the word\ + followed by the entries of its vectors, separated by blanks. If None is specified, the word embeddings are zero-initialized and trained\ + jointly with the classification task. [None]") neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= "Specify the number of layers and the respective number of units as a list. The i-th element of the list \ specifies the number of units in layer i. [1000]") diff --git a/Code/lucid_ml/utils/glove.6B.50d.txt b/Code/lucid_ml/utils/glove.6B.50d.txt new file mode 100644 index 0000000..3550154 --- /dev/null +++ b/Code/lucid_ml/utils/glove.6B.50d.txt @@ -0,0 +1,50000 @@ +the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 0.0027849 -0.18411 -0.11514 -0.78581 +, 0.013441 0.23682 -0.16899 0.40951 0.63812 0.47709 -0.42852 -0.55641 -0.364 -0.23938 0.13001 -0.063734 -0.39575 -0.48162 0.23291 0.090201 -0.13324 0.078639 -0.41634 -0.15428 0.10068 0.48891 0.31226 -0.1252 -0.037512 -1.5179 0.12612 -0.02442 -0.042961 -0.28351 3.5416 -0.11956 -0.014533 -0.1499 0.21864 -0.33412 -0.13872 0.31806 0.70358 0.44858 -0.080262 0.63003 0.32111 -0.46765 0.22786 0.36034 -0.37818 -0.56657 0.044691 0.30392 +. 0.15164 0.30177 -0.16763 0.17684 0.31719 0.33973 -0.43478 -0.31086 -0.44999 -0.29486 0.16608 0.11963 -0.41328 -0.42353 0.59868 0.28825 -0.11547 -0.041848 -0.67989 -0.25063 0.18472 0.086876 0.46582 0.015035 0.043474 -1.4671 -0.30384 -0.023441 0.30589 -0.21785 3.746 0.0042284 -0.18436 -0.46209 0.098329 -0.11907 0.23919 0.1161 0.41705 0.056763 -6.3681e-05 0.068987 0.087939 -0.10285 -0.13931 0.22314 -0.080803 -0.35652 0.016413 0.10216 +of 0.70853 0.57088 -0.4716 0.18048 0.54449 0.72603 0.18157 -0.52393 0.10381 -0.17566 0.078852 -0.36216 -0.11829 -0.83336 0.11917 -0.16605 0.061555 -0.012719 -0.56623 0.013616 0.22851 -0.14396 -0.067549 -0.38157 -0.23698 -1.7037 -0.86692 -0.26704 -0.2589 0.1767 3.8676 -0.1613 -0.13273 -0.68881 0.18444 0.0052464 -0.33874 -0.078956 0.24185 0.36576 -0.34727 0.28483 0.075693 -0.062178 -0.38988 0.22902 -0.21617 -0.22562 -0.093918 -0.80375 +to 0.68047 -0.039263 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-0.51988 -0.44209 0.51393 -0.025188 -0.89484 -0.18534 -0.43676 0.49268 -0.18536 -1.6341 -1.0299 0.92287 -0.41807 1.801 0.56901 1.1125 0.85743 0.69468 0.98245 0.5739 1.2601 1.6772 -0.16746 -0.3718 -0.14475 0.54759 0.0034697 -0.13527 0.52454 -0.80055 +llorente 0.84085 -0.34718 0.13696 -0.40256 0.73803 -0.59747 -0.3097 -0.27765 -0.23874 0.53012 0.60339 -0.75576 -0.39136 -0.40914 -0.041146 -1.3685 0.0040786 -0.59694 -0.30557 0.012022 -1.9322 -0.44967 -0.15716 0.49087 -0.14314 0.70779 2.2067 0.081067 -0.77222 -0.29031 0.1578 1.1476 -0.34392 -0.52951 -0.46025 0.37688 -0.36622 1.8649 0.73507 0.025407 1.4515 0.51064 -0.5941 -0.17463 -0.14063 -0.32966 0.74602 -0.17957 1.1076 0.41901 +.07 -0.047044 0.12109 1.4329 0.068752 -0.75878 0.65506 0.082999 -0.66655 -0.4356 1.1144 -0.74012 -0.75764 0.28449 -0.55097 0.11066 -0.66194 -0.35018 -0.31014 -1.4892 1.3756 -0.32679 0.65563 0.47999 -0.42419 0.32755 0.73549 1.0883 -0.50665 0.51626 0.30508 -0.018134 0.31011 0.32781 1.8648 -0.29784 -1.4284 0.24843 0.51804 0.83413 -0.24355 1.0011 0.04039 1.1292 0.68426 -0.43667 0.099719 -0.30185 0.11661 0.53111 0.85551 +rezko 0.56363 0.84653 0.5439 0.4899 0.45794 0.65036 -0.076632 0.14823 -0.80316 1.5714 -1.4841 0.27389 -0.033135 -0.57368 0.87725 -0.8803 -0.057461 0.21242 0.9185 0.7214 0.56717 -0.20553 -0.33317 -0.59111 -0.65845 -0.51538 1.3458 0.045397 0.23955 -0.77068 -0.83554 -0.14546 0.36023 -0.94018 -0.65278 -0.24505 -0.70915 0.047017 0.04713 -0.64082 -0.55182 0.44203 -0.92269 0.14192 -0.10966 -1.1027 -1.2246 -0.37125 -0.29411 0.67703 +sandford 0.92152 -0.54975 -0.42212 -0.090103 0.39801 0.43308 -0.12552 -0.59463 0.75417 -0.26693 -0.95206 1.9695 0.012251 -0.6806 -0.19394 0.033086 0.49608 -0.22823 -1.2432 0.039959 0.13304 0.25062 0.17691 0.051064 0.45179 0.24062 0.76344 -0.79702 -0.54288 -0.22794 -0.76685 -0.91099 0.32276 0.48919 0.59516 -0.77959 0.078149 -0.96769 0.12388 -0.6043 0.15229 -0.039601 0.0358 0.99151 -0.30148 0.52517 0.43384 -1.2605 0.047342 0.54088 +nanda 0.69149 -0.41556 -0.14521 0.84896 0.59023 -0.49154 0.7815 -0.3166 -0.58513 -0.78612 -0.15023 0.71353 0.56411 -0.44084 0.069801 0.37532 -0.49416 0.3578 0.66801 0.97791 -0.19697 -0.048598 -0.17773 -0.6147 1.4531 0.079971 -0.11254 -0.4028 -1.3125 -0.2437 -0.43307 -0.33524 0.52602 0.51737 0.19005 0.63083 -0.77241 0.11425 -0.28756 -0.14453 -1.0093 0.78398 -0.30576 -0.61435 -0.42607 -0.95274 0.73472 -1.3194 0.68682 -0.73028 diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index e1859bb..b315ca5 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -36,7 +36,6 @@ def _load_pretrained_vocabulary(filename): for i, line in enumerate(embedding_file.readlines()): row = line.strip().split(' ') mapping[row[0]] = i - print('Loaded Embedding!') return mapping def fit(self, X, y = None): From d8694b0981a5fb71c39b979faa15e6a6574d49ca Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 7 Aug 2017 17:47:04 +0200 Subject: [PATCH 053/116] Option for continuing to train the embedding layer alongside the task --- .../lucid_ml/classifying/tensorflow_models.py | 42 +++++++++++++------ Code/lucid_ml/run.py | 8 +++- 2 files changed, 35 insertions(+), 15 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 04c4719..06f23e7 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -22,14 +22,14 @@ def _load_embeddings(filename): embeddings = np.asarray(embeddings) return embeddings, embedding_size -def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = True): +def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = True, trainable_embeddings = True): with tf.device('/cpu:0'), tf.name_scope("embedding"): if pretrained_embeddings: lookup_table = tf.Variable(tf.constant(0.0, shape=[vocab_size, embedding_size]), - trainable=False, name="W") + trainable=trainable_embeddings, name="W") embedding_placeholder = tf.placeholder(tf.float32, [vocab_size, embedding_size]) embedding_init = lookup_table.assign(embedding_placeholder) else: @@ -54,19 +54,25 @@ def _extract_vocab_size(X): feature_input = tf.slice(x_tensor, [0, 0], [-1, sequence_length - 1]) return x_tensor, vocab_size, feature_input -def _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict): +def _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict, trainable_embeddings): if pretrained_embeddings_path is not None: embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path) - embedded_words, embedding_placeholder = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = True) + embedded_words, embedding_placeholder = _embeddings(feature_input, vocab_size, embedding_size, + pretrained_embeddings = True, + trainable_embeddings = trainable_embeddings) params_fit.update({embedding_placeholder : embeddings}) params_predict.update({embedding_placeholder : embeddings}) else: - embedded_words = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = False) + embedded_words = _embeddings(feature_input, vocab_size, embedding_size, + pretrained_embeddings = False, trainable_embeddings = trainable_embeddings) return embedded_words, embedding_size -def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True, pretrained_embeddings_path = None): +def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], + aggregate_output = True, + pretrained_embeddings_path = None, + trainable_embeddings = True): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -77,7 +83,10 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words, _ = _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict) + embedded_words, _ = _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, + params_fit, + params_predict, + trainable_embeddings) # build multiple layers of lstms stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) @@ -93,7 +102,9 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = hidden_layer = tf.nn.dropout(output_state, dropout_tensor) return x_tensor, y_tensor, hidden_layer, params_fit, params_predict -def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], pretrained_embeddings_path = None): +def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], + pretrained_embeddings_path = None, + trainable_embeddings = True): """Model function for CNN.""" # x_tensor includes the max_index_column, feature_input doesnt. go on with feature_input, but return x_tensor for feed_dict @@ -106,7 +117,10 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict) + embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, feature_input, + vocab_size, params_fit, + params_predict, + trainable_embeddings) # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling embedded_words = tf.expand_dims(embedded_words, -1) @@ -240,15 +254,17 @@ def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing) return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, self_normalizing = self_normalizing) -def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path = "utils/glove.6B.50d.txt"): +def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, - hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path) + hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, + trainable_embeddings = trainable_embeddings) -def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path = "utils/glove.6B.50d.txt"): +def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, - hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path) + hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, + trainable_embeddings = trainable_embeddings) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 4305d44..1d75f2e 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -522,7 +522,8 @@ def create_classifier(options, num_concepts): optimize_threshold = options.optimize_threshold, get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, - pretrained_embeddings_path = options.pretrained_embeddings)), + pretrained_embeddings_path = options.pretrained_embeddings, + trainable_embeddings=options.trainable_embeddings)), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -530,7 +531,8 @@ def create_classifier(options, num_concepts): optimize_threshold = options.optimize_threshold, get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, - pretrained_embeddings_path = options.pretrained_embeddings)), + pretrained_embeddings_path = options.pretrained_embeddings, + trainable_embeddings = options.trainable_embeddings)), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -710,6 +712,8 @@ def _generate_parsers(): "Specify the path to a file contraining pretrained word embeddings. The file must have a format where each line consists of the word\ followed by the entries of its vectors, separated by blanks. If None is specified, the word embeddings are zero-initialized and trained\ jointly with the classification task. [None]") + neural_network_options.add_argument('--trainable_embeddings', action="store_true", dest="trainable_embeddings", default=False, help= + "Whether to keep training the pretrained embeddings further with classification the task or not. [False]") neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= "Specify the number of layers and the respective number of units as a list. The i-th element of the list \ specifies the number of units in layer i. [1000]") From 9c260fb78d38c64dd64f6e73b4634da3b8c6acef Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 8 Aug 2017 10:17:30 +0200 Subject: [PATCH 054/116] Bugfix where embedding training from scratch wasnt working --- Code/lucid_ml/classifying/tensorflow_models.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 06f23e7..5202952 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -54,7 +54,7 @@ def _extract_vocab_size(X): feature_input = tf.slice(x_tensor, [0, 0], [-1, sequence_length - 1]) return x_tensor, vocab_size, feature_input -def _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, params_fit, params_predict, trainable_embeddings): +def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_size, vocab_size, params_fit, params_predict, trainable_embeddings): if pretrained_embeddings_path is not None: embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path) @@ -83,7 +83,8 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words, _ = _init_embedding_layer(pretrained_embeddings_path, feature_input, vocab_size, + embedded_words, _ = _init_embedding_layer(pretrained_embeddings_path, feature_input, + embedding_size, vocab_size, params_fit, params_predict, trainable_embeddings) @@ -117,7 +118,8 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} - embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, feature_input, + embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, + feature_input, embedding_size, vocab_size, params_fit, params_predict, trainable_embeddings) From 12a6f141becc6d2bb483a1bc773e11293fe78a4a Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 8 Aug 2017 10:48:24 +0200 Subject: [PATCH 055/116] Print longest sequence of epochs with no improvement --- Code/lucid_ml/classifying/tensorflow_models.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 5202952..551206e 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -422,6 +422,7 @@ def fit(self, X, y): avg_validation_score = math.inf * objective best_validation_score = math.inf * objective epochs_of_no_improvement = 0 + most_consecutive_epochs_with_no_improvement = 0 for epoch in range(self.num_epochs): if val_pos is not None and epochs_of_no_improvement == self.tolerance: @@ -473,6 +474,9 @@ def fit(self, X, y): best_validation_score = avg_validation_score saver = tf.train.Saver() saver.save(session, self._save_model_path) + + if most_consecutive_epochs_with_no_improvement < epochs_of_no_improvement: + most_consecutive_epochs_with_no_improvement = epochs_of_no_improvement epochs_of_no_improvement = 0 # save the threshold at best model, too. @@ -490,6 +494,9 @@ def fit(self, X, y): self.session = session print('') + + print("Training of TensorFlow model finished!") + print("Longest sequence of epochs of no improvement:", most_consecutive_epochs_with_no_improvement) #def model_fn_with_num_classes(features, targets, mode, params): # return self.model_fn(features, targets, mode, params, y.shape[1]) From 8bbb9a55eca07399b216268fdcf86cbcfcc12f3c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 8 Aug 2017 13:25:46 +0200 Subject: [PATCH 056/116] Fixed an issue when loading word embeddings --- .../lucid_ml/classifying/tensorflow_models.py | 19 +++++++++++----- Code/lucid_ml/run.py | 3 ++- Code/lucid_ml/utils/text_encoding.py | 22 +++++++++++++------ 3 files changed, 30 insertions(+), 14 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 5202952..617a8b7 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -11,15 +11,22 @@ from datetime import datetime #tf.logging.set_verbosity(tf.logging.INFO) -def _load_embeddings(filename): +def _load_embeddings(filename, vocab_size, embedding_size): - embeddings = [] + embeddings = np.zeros((vocab_size, embedding_size)) with open(filename,'r') as embedding_file: + # skip first line, which we dont need + embedding_file.readline() + + i = 0 for line in embedding_file.readlines(): row = line.strip().split(' ') - embeddings.append(row[1:]) - embedding_size = len(embeddings[0]) - embeddings = np.asarray(embeddings) + # omit escape sequences + if len(row) != embedding_size + 1: + continue + else: + embeddings[i, :] = np.asarray(row[1:], dtype=np.float32) + i += 1 return embeddings, embedding_size def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = True, trainable_embeddings = True): @@ -56,7 +63,7 @@ def _extract_vocab_size(X): def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_size, vocab_size, params_fit, params_predict, trainable_embeddings): if pretrained_embeddings_path is not None: - embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path) + embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path, vocab_size, embedding_size) embedded_words, embedding_placeholder = _embeddings(feature_input, vocab_size, embedding_size, pretrained_embeddings = True, diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 1d75f2e..741e6c8 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -136,7 +136,8 @@ def _build_features(options): elif options.concepts: extractor = concepts elif options.onehot: - extractor = TextEncoder(input_format = "filename" if options.fulltext else "content", max_words=options.max_features) + extractor = TextEncoder(input_format = "filename" if options.fulltext else "content", + max_words=options.max_features, pretrained = options.pretrained_embeddings) else: raise ValueError("No feature representation specified!") diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index b315ca5..e737bc1 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -9,7 +9,7 @@ class TextEncoder(BaseEstimator, TransformerMixin): - def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None, pretrained = "utils/glove.6B.50d.txt"): + def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None, pretrained = None): self.tokenize = tokenize self.input = input_format @@ -33,10 +33,20 @@ def _limit_num_words(self, words, max_length): def _load_pretrained_vocabulary(filename): mapping = {} with open(filename,'r') as embedding_file: - for i, line in enumerate(embedding_file.readlines()): + embedding_size = int(embedding_file.readline().strip().split(" ")[1]) + + i = 0 + for line in embedding_file.readlines(): row = line.strip().split(' ') - mapping[row[0]] = i - return mapping + + # make sure we dont use escape sequences and so on + if len(row) == embedding_size + 1: + if row[0] in mapping: + print(row[0], "is already in mapping") + else: + mapping[row[0]] = i + i += 1 + return mapping, i def fit(self, X, y = None): """ @@ -71,7 +81,7 @@ def fit(self, X, y = None): else: - mapping = TextEncoder._load_pretrained_vocabulary(self.pretrained) + mapping, max_index = TextEncoder._load_pretrained_vocabulary(self.pretrained) max_length = 0 for text in X: @@ -84,8 +94,6 @@ def fit(self, X, y = None): if len(words) > max_length: max_length = len(words) - - max_index = len(mapping) # save variables required for transformation step self.mapping = mapping From ec8863ac44c31c3a82edcfb6d9312e7b16bf5af3 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 8 Aug 2017 13:44:57 +0200 Subject: [PATCH 057/116] Patience for early stopping is now a program argument --- Code/lucid_ml/classifying/tensorflow_models.py | 10 +++++----- Code/lucid_ml/run.py | 10 ++++++++-- 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 551206e..b5548de 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -317,7 +317,7 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, tolerance = 5, + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, patience = 5, validation_metric = lambda y1, y2 : f1_score(y1, y2, average = "samples"), optimize_threshold = True, threshold_window = np.linspace(-0.03, 0.03, num=7), @@ -335,7 +335,7 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), t self.validation_metric = validation_metric self.optimize_threshold = optimize_threshold self.threshold_window = threshold_window - self.tolerance = tolerance + self.patience = patience self.batch_size = batch_size self.num_epochs = num_epochs self.threshold = threshold @@ -425,7 +425,7 @@ def fit(self, X, y): most_consecutive_epochs_with_no_improvement = 0 for epoch in range(self.num_epochs): - if val_pos is not None and epochs_of_no_improvement == self.tolerance: + if val_pos is not None and epochs_of_no_improvement == self.patience: break # Loop over all batches @@ -484,8 +484,8 @@ def fit(self, X, y): self.threshold = best_threshold else: epochs_of_no_improvement += 1 - if epochs_of_no_improvement > self.tolerance: - print("No improvement in validation loss for", self.tolerance, "epochs. Stopping early.") + if epochs_of_no_improvement > self.patience: + print("No improvement in validation loss for", self.patience, "epochs. Stopping early.") break # print progress diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 1d75f2e..7e02f0a 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -507,19 +507,22 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, - get_model = mlp_base(options.dropout)), + get_model = mlp_base(options.dropout), + patience = options.patience), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, get_model = mlp_soph(options.dropout, options.embedding_size, - hidden_layers = options.hidden_layers, self_normalizing = options.snn)), + hidden_layers = options.hidden_layers, self_normalizing = options.snn), + patience = options.patience), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, + patience = options.patience, get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, @@ -529,6 +532,7 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, + patience = options.patience, get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, @@ -683,6 +687,8 @@ def _generate_parsers(): "Performs Grid search to find optimal K") classifier_options.add_argument('-e', type=int, dest="max_iterations", default=5, help= "Determine the number of epochs for the training of several classifiers [5]") + classifier_options.add_argument('--patience', type=int, dest="patience", default=5, help= + "Specify the number of epochs of no improvement in validation score before training is stopped. [5]") classifier_options.add_argument('--learning_rate', type=float, dest="learning_rate", default=None, help= "Determine the learning rate for training of several classifiers. If set to 'None', the learning rate is automatically based on an empirical good value and \ adapted to the batch size. [None]") From 8055b2fd09b94e2766462f81c376df3ae51aa47d Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 14 Aug 2017 19:28:57 +0200 Subject: [PATCH 058/116] Fixed an issue with initializing word embeddings --- .../lucid_ml/classifying/tensorflow_models.py | 23 ++++++------------- Code/lucid_ml/utils/glove.6B.50d.txt | 1 + 2 files changed, 8 insertions(+), 16 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 78edf95..b739974 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -33,23 +33,16 @@ def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = Tr with tf.device('/cpu:0'), tf.name_scope("embedding"): - if pretrained_embeddings: - - lookup_table = tf.Variable(tf.constant(0.0, shape=[vocab_size, embedding_size]), + if pretrained_embeddings is not None: + lookup_table = tf.Variable(tf.constant(pretrained_embeddings, shape=[vocab_size, embedding_size], dtype = tf.float32), trainable=trainable_embeddings, name="W") - embedding_placeholder = tf.placeholder(tf.float32, [vocab_size, embedding_size]) - embedding_init = lookup_table.assign(embedding_placeholder) else: lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name="W") - embedding_init = lookup_table - embedded_words = tf.nn.embedding_lookup(embedding_init, x_tensor) + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) - if pretrained_embeddings: - return embedded_words, embedding_placeholder - else: - return embedded_words + return embedded_words def _extract_vocab_size(X): # max index of vocabulary is encoded in last column @@ -65,14 +58,12 @@ def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_s if pretrained_embeddings_path is not None: embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path, vocab_size, embedding_size) - embedded_words, embedding_placeholder = _embeddings(feature_input, vocab_size, embedding_size, - pretrained_embeddings = True, + embedded_words = _embeddings(feature_input, vocab_size, embedding_size, + pretrained_embeddings = embeddings, trainable_embeddings = trainable_embeddings) - params_fit.update({embedding_placeholder : embeddings}) - params_predict.update({embedding_placeholder : embeddings}) else: embedded_words = _embeddings(feature_input, vocab_size, embedding_size, - pretrained_embeddings = False, trainable_embeddings = trainable_embeddings) + pretrained_embeddings = None, trainable_embeddings = trainable_embeddings) return embedded_words, embedding_size diff --git a/Code/lucid_ml/utils/glove.6B.50d.txt b/Code/lucid_ml/utils/glove.6B.50d.txt index 3550154..e8b0b0b 100644 --- a/Code/lucid_ml/utils/glove.6B.50d.txt +++ b/Code/lucid_ml/utils/glove.6B.50d.txt @@ -1,3 +1,4 @@ +50000 50 the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 0.0027849 -0.18411 -0.11514 -0.78581 , 0.013441 0.23682 -0.16899 0.40951 0.63812 0.47709 -0.42852 -0.55641 -0.364 -0.23938 0.13001 -0.063734 -0.39575 -0.48162 0.23291 0.090201 -0.13324 0.078639 -0.41634 -0.15428 0.10068 0.48891 0.31226 -0.1252 -0.037512 -1.5179 0.12612 -0.02442 -0.042961 -0.28351 3.5416 -0.11956 -0.014533 -0.1499 0.21864 -0.33412 -0.13872 0.31806 0.70358 0.44858 -0.080262 0.63003 0.32111 -0.46765 0.22786 0.36034 -0.37818 -0.56657 0.044691 0.30392 . 0.15164 0.30177 -0.16763 0.17684 0.31719 0.33973 -0.43478 -0.31086 -0.44999 -0.29486 0.16608 0.11963 -0.41328 -0.42353 0.59868 0.28825 -0.11547 -0.041848 -0.67989 -0.25063 0.18472 0.086876 0.46582 0.015035 0.043474 -1.4671 -0.30384 -0.023441 0.30589 -0.21785 3.746 0.0042284 -0.18436 -0.46209 0.098329 -0.11907 0.23919 0.1161 0.41705 0.056763 -6.3681e-05 0.068987 0.087939 -0.10285 -0.13931 0.22314 -0.080803 -0.35652 0.016413 0.10216 From 1561b8305eb86f1aee0f5372a28cb1be71001c0c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 15 Aug 2017 13:19:44 +0200 Subject: [PATCH 059/116] Feed the embedding lookup table at variable initialization to avoid limit on TF constant sizes --- .../lucid_ml/classifying/tensorflow_models.py | 47 ++++++++++++------- 1 file changed, 31 insertions(+), 16 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index b739974..033312d 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -33,16 +33,21 @@ def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = Tr with tf.device('/cpu:0'), tf.name_scope("embedding"): - if pretrained_embeddings is not None: - lookup_table = tf.Variable(tf.constant(pretrained_embeddings, shape=[vocab_size, embedding_size], dtype = tf.float32), + if pretrained_embeddings: + embedding_placeholder = tf.placeholder(tf.float32, shape=[vocab_size, embedding_size]) + lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), trainable=trainable_embeddings, name="W") + embedding_init = tf.assign(lookup_table, embedding_placeholder) + + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) + return embedded_words, embedding_init, embedding_placeholder + else: lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name="W") + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) + return embedded_words - embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) - - return embedded_words def _extract_vocab_size(X): # max index of vocabulary is encoded in last column @@ -54,16 +59,19 @@ def _extract_vocab_size(X): feature_input = tf.slice(x_tensor, [0, 0], [-1, sequence_length - 1]) return x_tensor, vocab_size, feature_input -def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_size, vocab_size, params_fit, params_predict, trainable_embeddings): +def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_size, vocab_size, + params_fit, params_predict, trainable_embeddings, initializer_ops): if pretrained_embeddings_path is not None: embeddings, embedding_size = _load_embeddings(pretrained_embeddings_path, vocab_size, embedding_size) - embedded_words = _embeddings(feature_input, vocab_size, embedding_size, - pretrained_embeddings = embeddings, + embedded_words, embedding_init, embedding_placeholder = _embeddings(feature_input, vocab_size, embedding_size, + pretrained_embeddings = True, trainable_embeddings = trainable_embeddings) + initializer_ops.append((embedding_init, {embedding_placeholder : embeddings})) + else: embedded_words = _embeddings(feature_input, vocab_size, embedding_size, - pretrained_embeddings = None, trainable_embeddings = trainable_embeddings) + pretrained_embeddings = False, trainable_embeddings = trainable_embeddings) return embedded_words, embedding_size @@ -81,11 +89,14 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} + initializer_operations = [] + embedded_words, _ = _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_size, vocab_size, params_fit, params_predict, - trainable_embeddings) + trainable_embeddings, + initializer_operations) # build multiple layers of lstms stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) @@ -100,7 +111,7 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = hidden_layer = tf.nn.dropout(output_state, dropout_tensor) - return x_tensor, y_tensor, hidden_layer, params_fit, params_predict + return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, initializer_operations def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], pretrained_embeddings_path = None, trainable_embeddings = True): @@ -116,11 +127,13 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ params_fit = {dropout_tensor : 1 - keep_prob_dropout} params_predict = {dropout_tensor : 1} + initializer_operations = [] embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_size, vocab_size, params_fit, params_predict, - trainable_embeddings) + trainable_embeddings, + initializer_operations) # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling embedded_words = tf.expand_dims(embedded_words, -1) @@ -150,7 +163,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) - return x_tensor, y_tensor, hidden_layer, params_fit, params_predict + return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, initializer_operations def mlp_base_fn(X, y, keep_prob_dropout = 0.5): """Model function for MLP-Soph.""" @@ -167,7 +180,7 @@ def mlp_base_fn(X, y, keep_prob_dropout = 0.5): hidden_layer = tf.contrib.layers.relu(x_tensor, 1000) hidden_dropout = tf.nn.dropout(hidden_layer, dropout_tensor) - return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict + return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict, [] # https://github.com/bioinf-jku/SNNs/blob/master/selu.py def dropout_selu(x, rate, alpha= -1.7580993408473766, fixedPointMean=0.0, fixedPointVar=1.0, @@ -245,7 +258,7 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer hidden_layer = selu(hidden_layer) hidden_layer = dropout_selu(hidden_layer, dropout_tensor) - return x_tensor, y_tensor, hidden_layer, params_fit, params_predict + return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, [] def mlp_base(keep_prob_dropout): return lambda X, y : mlp_base_fn(X, y, keep_prob_dropout = keep_prob_dropout) @@ -394,7 +407,7 @@ def fit(self, X, y): # Inputs # get_model has to return a - self.x_tensor, self.y_tensor, self.last_layer, self.params_fit, self.params_predict = self.get_model(X, y) + self.x_tensor, self.y_tensor, self.last_layer, self.params_fit, self.params_predict, initializer_operations = self.get_model(X, y) # Name logits Tensor, so that is can be loaded from disk after training #logits = tf.identity(logits, name='logits') @@ -413,6 +426,8 @@ def fit(self, X, y): session = tf.Session() # Initializing the variables session.run(tf.global_variables_initializer()) + for (init_op, init_op_feed_dict) in initializer_operations: + session.run(init_op, feed_dict = init_op_feed_dict) batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) # Training cycle From 042177347c65dd8e4a936086d7a2e4934ba6b522 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 16 Aug 2017 12:28:27 +0200 Subject: [PATCH 060/116] Print information on label frequency when verbose --- Code/lucid_ml/run.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 2bcdf79..2515cbc 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -85,7 +85,12 @@ def _build_features(options): mlb = MultiLabelBinarizer(sparse_output=True, classes=[i[1] for i in sorted( tr.index_nodename.items())] if options.hierarch_f1 else None) Y = mlb.fit_transform(Y_raw) - if VERBOSE: print("Y = " + str(Y.shape)) + + if VERBOSE: + print("Y = " + str(Y.shape)) + y_sum = Y.sum(axis = 0) + for i in range(1, 5): + print("Number of labels assigned more than", i, "times:" , np.sum(y_sum > i)) # --- EXTRACT FEATURES --- input_format = 'filename' if options.fulltext else 'content' From 4f4ed04e2a743d1e230b551d9d0c64a0226760f4 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 17 Aug 2017 09:56:22 +0200 Subject: [PATCH 061/116] Keep prob now interpreted correctly, activation fn is a parameter --- .../lucid_ml/classifying/tensorflow_models.py | 37 +++++++++++++------ Code/lucid_ml/run.py | 4 +- 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 033312d..c83a0f8 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -86,7 +86,7 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") - params_fit = {dropout_tensor : 1 - keep_prob_dropout} + params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} initializer_operations = [] @@ -124,7 +124,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") - params_fit = {dropout_tensor : 1 - keep_prob_dropout} + params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} initializer_operations = [] @@ -165,7 +165,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, initializer_operations -def mlp_base_fn(X, y, keep_prob_dropout = 0.5): +def mlp_base_fn(X, y, keep_prob_dropout = 0.5, hidden_activation_function = tf.nn.relu): """Model function for MLP-Soph.""" # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") @@ -177,7 +177,7 @@ def mlp_base_fn(X, y, keep_prob_dropout = 0.5): # Connect the first hidden layer to input layer # (features) with relu activation and add dropout - hidden_layer = tf.contrib.layers.relu(x_tensor, 1000) + hidden_layer = tf.contrib.layers.fully_connected(x_tensor, 1000, activation_fn = hidden_activation_function) hidden_dropout = tf.nn.dropout(hidden_layer, dropout_tensor) return x_tensor, y_tensor, hidden_dropout, params_fit, params_predict, [] @@ -227,14 +227,14 @@ def selu(x): scale = 1.0507009873554804934193349852946 return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x)) -def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = True): +def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = True, hidden_activation_function = tf.nn.relu): """Model function for MLP-Soph.""" # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") - params_fit = {dropout_tensor : 1 - keep_prob_dropout} + params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} # apply a look-up as described by the fastText paper @@ -249,23 +249,36 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer hidden_layer = embedding_layer for hidden_units in hidden_layers: if not self_normalizing: - hidden_layer = tf.contrib.layers.relu(hidden_layer, hidden_units) + hidden_layer = tf.contrib.layers.fully_connected(hidden_layer, hidden_units, activation_fn = hidden_activation_function) hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) else: hidden_layer = tf.contrib.layers.fully_connected(hidden_layer, hidden_units, activation_fn=None, weights_initializer=tf.contrib.layers.variance_scaling_initializer(factor=1.0, mode='FAN_IN')) hidden_layer = selu(hidden_layer) - hidden_layer = dropout_selu(hidden_layer, dropout_tensor) + # dropout_selu expects to be given the dropout rate instead of keep probability + hidden_layer = dropout_selu(hidden_layer, tf.constant(1, tf.float32) - dropout_tensor) return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, [] -def mlp_base(keep_prob_dropout): - return lambda X, y : mlp_base_fn(X, y, keep_prob_dropout = keep_prob_dropout) +def _transform_activation_function(func): + if func == "relu": + hidden_activation_function = tf.nn.relu + elif func == "tanh": + hidden_activation_function = tf.nn.tanh + return hidden_activation_function -def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing): +def mlp_base(keep_prob_dropout, hidden_activation_function = "relu"): + hidden_activation_function = _transform_activation_function(hidden_activation_function) + + return lambda X, y : mlp_base_fn(X, y, keep_prob_dropout = keep_prob_dropout, hidden_activation_function=hidden_activation_function) + +def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, hidden_activation_function = "relu"): + hidden_activation_function = _transform_activation_function(hidden_activation_function) + return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, - hidden_layers = hidden_layers, self_normalizing = self_normalizing) + hidden_layers = hidden_layers, self_normalizing = self_normalizing, + hidden_activation_function=hidden_activation_function) def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 2515cbc..cf2fb26 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -513,7 +513,7 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, - get_model = mlp_base(options.dropout), + get_model = mlp_base(options.dropout, hidden_activation_function = options.hidden_activation_function), patience = options.patience), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, @@ -724,6 +724,8 @@ def _generate_parsers(): "Specify the path to a file contraining pretrained word embeddings. The file must have a format where each line consists of the word\ followed by the entries of its vectors, separated by blanks. If None is specified, the word embeddings are zero-initialized and trained\ jointly with the classification task. [None]") + neural_network_options.add_argument('--hidden_activation_function', type=str, dest="hidden_activation_function", default="relu", help= + "Specify the activation function used on the hidden layers in MLP-Base and MLP-Soph. [relu]", choices = ["relu", "tanh"]) neural_network_options.add_argument('--trainable_embeddings', action="store_true", dest="trainable_embeddings", default=False, help= "Whether to keep training the pretrained embeddings further with classification the task or not. [False]") neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= From 01291ebbf8a24bd3c391cbe670ada436f518f5f5 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 17 Aug 2017 13:02:58 +0200 Subject: [PATCH 062/116] Added option for normalizing in MLP Soph --- .../lucid_ml/classifying/tensorflow_models.py | 32 ++++++++++++++++--- Code/lucid_ml/run.py | 7 ++-- 2 files changed, 32 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index c83a0f8..d142b1c 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -227,8 +227,10 @@ def selu(x): scale = 1.0507009873554804934193349852946 return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x)) -def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = True, hidden_activation_function = tf.nn.relu): +def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = False, hidden_activation_function = tf.nn.relu, + standard_normal = False): """Model function for MLP-Soph.""" + # convert sparse tensors to dense x_tensor = tf.placeholder(tf.float32, shape=(None, X.shape[1]), name = "x") y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") @@ -237,12 +239,31 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} + # we need to have the input data scaled such they have mean 0 and variance 1 + if self_normalizing or standard_normal: + m = X.mean(axis = 0) + + # because we are dealing with sparse matrices, we need to compute the variance as + # E[X^2] - E[X]^2 + X_square = X.power(2) + m_square = X_square.mean(axis = 0) + v = m_square - np.power(m, 2) + s = np.sqrt(v) + + + m = tf.constant(m, dtype = tf.float32) + s = tf.constant(s, dtype = tf.float32) + + scaled_input = (x_tensor - m) / s + else: + scaled_input = x_tensor + # apply a look-up as described by the fastText paper if embedding_size > 0: lookup_table = tf.Variable(tf.truncated_normal([X.shape[1], embedding_size], mean=0.0, stddev=0.1)) - embedding_layer = tf.matmul(x_tensor, lookup_table) + embedding_layer = tf.matmul(scaled_input, lookup_table) else: - embedding_layer = x_tensor + embedding_layer = scaled_input # Connect the embedding layer to the hidden layers # (features) with relu activation and add dropout everywhere @@ -273,12 +294,13 @@ def mlp_base(keep_prob_dropout, hidden_activation_function = "relu"): return lambda X, y : mlp_base_fn(X, y, keep_prob_dropout = keep_prob_dropout, hidden_activation_function=hidden_activation_function) -def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, hidden_activation_function = "relu"): +def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, standard_normal, hidden_activation_function = "relu"): hidden_activation_function = _transform_activation_function(hidden_activation_function) return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, self_normalizing = self_normalizing, - hidden_activation_function=hidden_activation_function) + hidden_activation_function=hidden_activation_function, + standard_normal = standard_normal) def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index cf2fb26..01b6464 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -520,8 +520,9 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, - get_model = mlp_soph(options.dropout, options.embedding_size, - hidden_layers = options.hidden_layers, self_normalizing = options.snn), + get_model = mlp_soph(options.dropout, options.embedding_size, + hidden_layers = options.hidden_layers, self_normalizing = options.snn, + standard_normal = options.standard_normal), patience = options.patience), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, @@ -731,6 +732,8 @@ def _generate_parsers(): neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= "Specify the number of layers and the respective number of units as a list. The i-th element of the list \ specifies the number of units in layer i. [1000]") + neural_network_options.add_argument('--standard_normal', action="store_true", dest="standard_normal", default=False, help= + "Whether to normalize the input features to mean = 0 and std = 1 for MLPSoph. [False]") neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= "Whether to use SELU activation and -dropout. If set to False, RELU activation is used. [False]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= From 24cd0baddb3dc09c46af5a7a763ac617a36164a5 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 17 Aug 2017 13:09:16 +0200 Subject: [PATCH 063/116] Define MLP-Base as special case of MLPsoph --- Code/lucid_ml/classifying/tensorflow_models.py | 11 +++++++---- Code/lucid_ml/run.py | 2 +- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index d142b1c..3ec4375 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -289,11 +289,14 @@ def _transform_activation_function(func): hidden_activation_function = tf.nn.tanh return hidden_activation_function -def mlp_base(keep_prob_dropout, hidden_activation_function = "relu"): +def mlp_base(hidden_activation_function = "relu"): hidden_activation_function = _transform_activation_function(hidden_activation_function) - return lambda X, y : mlp_base_fn(X, y, keep_prob_dropout = keep_prob_dropout, hidden_activation_function=hidden_activation_function) - + return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 0, + hidden_layers = [1000], self_normalizing = False, + hidden_activation_function=hidden_activation_function, + standard_normal = False) + def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, standard_normal, hidden_activation_function = "relu"): hidden_activation_function = _transform_activation_function(hidden_activation_function) @@ -363,7 +366,7 @@ class MultiLabelSKFlow(BaseEstimator): infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. """ - def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(0.5), threshold = 0.2, learning_rate = 0.1, patience = 5, + def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), threshold = 0.2, learning_rate = 0.1, patience = 5, validation_metric = lambda y1, y2 : f1_score(y1, y2, average = "samples"), optimize_threshold = True, threshold_window = np.linspace(-0.03, 0.03, num=7), diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 01b6464..055de32 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -513,7 +513,7 @@ def create_classifier(options, num_concepts): learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, - get_model = mlp_base(options.dropout, hidden_activation_function = options.hidden_activation_function), + get_model = mlp_base(hidden_activation_function = options.hidden_activation_function), patience = options.patience), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, From efd5f39d36a39d7202558f47b6c34f0105942fda Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 17 Aug 2017 14:54:03 +0200 Subject: [PATCH 064/116] Grid search as optimization technique --- Code/lucid_ml/default_searchspace | 10 ++++++- Code/lucid_ml/run.py | 44 +++++++++++++++++++++++++++++-- 2 files changed, 51 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/default_searchspace b/Code/lucid_ml/default_searchspace index 659da1c..2f52ae9 100644 --- a/Code/lucid_ml/default_searchspace +++ b/Code/lucid_ml/default_searchspace @@ -14,4 +14,12 @@ dropout,0.05,0.95,uniform # <parameter_name_n>,<lower_bound_n>,<upper_bound_n>,<init_value_n> # example: #learning_rate,0.0001,1,0.1 -#dropout,0.05,0.95,0.05 \ No newline at end of file +#dropout,0.05,0.95,0.05 +# +# for grid search, specify in the following format: +# <parameter_name_1>,<type_1>,<value_1>,..,<value_n_1> +# ... +# <parameter_name_m>,<type_m>,<value_1>,...,<value_n_m> +# example: +#learning_rate,float,0.01,0.005,0.001 +#dropout,float,0.5,0.05,0.95 \ No newline at end of file diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 055de32..4694ecc 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -17,6 +17,8 @@ from utils.processify import processify +from itertools import product + warnings.filterwarnings("ignore", category=UserWarning) from sklearn.model_selection import KFold, ShuffleSplit @@ -375,9 +377,10 @@ def _make_space(options): line = line.strip() info = line.split(",") param_name = info[0] - left_bound, right_bound = float(info[1]), float(info[2]) if options.optimization == "random": + left_bound, right_bound = float(info[1]), float(info[2]) + param_type = info[3] try: @@ -388,15 +391,45 @@ def _make_space(options): space[param_name] = param_type(param_name, left_bound, right_bound) elif options.optimization == "bayesian": + left_bound, right_bound = float(info[1]), float(info[2]) + init_val = float(info[3]) inits[param_name] = [init_val] space[param_name] = (left_bound, right_bound) + + elif options.optimization == "grid": + + param_type = info[1] + if param_type == "int": + cast_func = int + elif param_type == "float": + cast_func = float + + # all possible values + space[param_name] = list(map(cast_func, info[2:])) if options.optimization == "bayesian": return space, inits else: return space +def _all_option_combinations(space): + + names = [name for name, _ in space.items()] + values = [values for _, values in space.items()] + + val_combinations = product(*values) + + combinations = [] + for combi in val_combinations: + new_param_dict = {} + for i, val in enumerate(combi): + new_param_dict[names[i]] = val + + combinations.append(new_param_dict) + + return combinations + def run(options): VERBOSE = options.verbose @@ -444,6 +477,13 @@ def optimized_experiment(**parameters): algo=rand.suggest, max_evals=options.optimization_iterations, rstate = np.random.RandomState(1337)) + + elif options.optimization == "grid": + # perform grid-search by running every possible parameter combination + combinations = _all_option_combinations(_make_space(options)) + for combi in combinations: + optimized_experiment(**combi) + else: results = _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, options) @@ -620,7 +660,7 @@ def _generate_parsers(): detailed_options.add_argument('--test-size', type=float, dest='test_size', default=0.1, help= "Desired relative size for the test set [0.1]") detailed_options.add_argument('--optimization', type=str, dest='optimization', default=None, help= - "Whether to use Random Search or Bayesian Optimization for hyperparameter search. [None]", choices = ["random", "bayesian", None]) + "Whether to use Random Search or Bayesian Optimization for hyperparameter search. [None]", choices = ["grid", "random", "bayesian", None]) detailed_options.add_argument('--optimization_spaces', type=str, dest='optimization_spaces', default="default_searchspace", help= "Path to a file that specifies the search spaces for hyperparameters [default_searchspace]") detailed_options.add_argument('--optimization_iterations', type=int, dest='optimization_iterations', default=10, help= From 08a00949d6c428838f5e3335c87d9dd845006c37 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 17 Aug 2017 18:36:23 +0200 Subject: [PATCH 065/116] Bayesian optimization can take more than one initial value --- Code/lucid_ml/default_searchspace | 12 ++++++------ Code/lucid_ml/run.py | 13 +++++++------ 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/Code/lucid_ml/default_searchspace b/Code/lucid_ml/default_searchspace index 2f52ae9..afc565e 100644 --- a/Code/lucid_ml/default_searchspace +++ b/Code/lucid_ml/default_searchspace @@ -5,16 +5,16 @@ # ... # <parameter_name_n>,<lower_bound_n>,<upper_bound_n>,<space_type_n> # example: -learning_rate,-9.21,-2.3,loguniform -dropout,0.05,0.95,uniform +#learning_rate,-9.21,-2.3,loguniform +#dropout,0.05,0.95,uniform # # for bayesian optimization, specify in the following format: -# <parameter_name_1>,<lower_bound_1>,<upper_bound_1>,<init_value_1> +# <parameter_name_1>,<lower_bound_1>,<upper_bound_1>,<init_value_1_1>,<init_value_1_m> # ... -# <parameter_name_n>,<lower_bound_n>,<upper_bound_n>,<init_value_n> +# <parameter_name_n>,<lower_bound_n>,<upper_bound_n>,<init_value_n>,<init_value_n_m> # example: -#learning_rate,0.0001,1,0.1 -#dropout,0.05,0.95,0.05 +learning_rate,0.0001,1,0.1,0.01,0.111 +dropout,0.05,0.95,0.05,0.5,0.555 # # for grid search, specify in the following format: # <parameter_name_1>,<type_1>,<value_1>,..,<value_n_1> diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 4694ecc..95ffe86 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -352,7 +352,7 @@ def _run_experiment(X, Y, kf, validation_set_indices, mlb, X_raw, Y_raw, tr, opt def _update_options(options, **parameters): for param_name, param_value in parameters.items(): - print("I'm a param:", param_name, "with value", param_value) + print("In automatic optimization trying parameter:", param_name, "with value", param_value) try: setattr(options, param_name, param_value) @@ -393,8 +393,9 @@ def _make_space(options): elif options.optimization == "bayesian": left_bound, right_bound = float(info[1]), float(info[2]) - init_val = float(info[3]) - inits[param_name] = [init_val] + init_values = list(map(float, info[3:])) + num_init_vals = len(init_values) + inits[param_name] = init_values space[param_name] = (left_bound, right_bound) elif options.optimization == "grid": @@ -409,7 +410,7 @@ def _make_space(options): space[param_name] = list(map(cast_func, info[2:])) if options.optimization == "bayesian": - return space, inits + return space, inits, num_init_vals else: return space @@ -463,10 +464,10 @@ def optimized_experiment(**parameters): if options.optimization == "bayesian": gp_params = {"alpha": 1e-5, "kernel" : Matern(nu = 5 / 2)} - space, init_vals = _make_space(options) + space, init_vals, num_init_vals = _make_space(options) bayesian_optimizer = BayesianOptimization(optimized_experiment, space) bayesian_optimizer.explore(init_vals) - bayesian_optimizer.maximize(n_iter=options.optimization_iterations - 1, + bayesian_optimizer.maximize(n_iter=options.optimization_iterations - num_init_vals, acq = 'ei', **gp_params) From 7d03b317200b6da1730d258b8637e10470c68fde Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 24 Aug 2017 20:53:32 +0200 Subject: [PATCH 066/116] Option for using n-grams for BoW --- Code/lucid_ml/run.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 95ffe86..2a3e7de 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -104,9 +104,11 @@ def _build_features(options): persist_dir=options.persist_to, repersist=options.repersist, file_path=DATA_PATHS[options.data_key]['X']).analyze concepts = CountVectorizer(input=input_format, analyzer=concept_analyzer, binary=options.binary, - vocabulary=tr.nodename_index if not options.synsets else None) + vocabulary=tr.nodename_index if not options.synsets else None, + ngram_range=(1, options.ngram_limit)) terms = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, - token_pattern=word_regexp, max_features=options.max_features) + token_pattern=word_regexp, max_features=options.max_features, + ngram_range=(1, options.ngram_limit)) if options.hierarchical: hierarchy = tr.nx_graph @@ -715,6 +717,8 @@ def _generate_parsers(): "Do not use IDF") feature_options.add_argument('--no-norm', action="store_false", dest="norm", default=True, help="Do not normalize values") + feature_options.add_argument('--ngram_limit', type=int, dest="ngram_limit", default=1, help= \ + "Specify the n for n-grams to take into account for BoW vectorization. [1]") # group for classifiers classifier_options = parser.add_argument_group("Classifier Options") From 52a7e7f237409fccaa2d6a4f5b573026493adb9b Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 27 Aug 2017 09:33:31 +0200 Subject: [PATCH 067/116] Removed CPU scope which prevented GPU usage when using embedding lookup --- .../lucid_ml/classifying/tensorflow_models.py | 30 +++++++++---------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 3ec4375..382c3df 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -31,23 +31,21 @@ def _load_embeddings(filename, vocab_size, embedding_size): def _embeddings(x_tensor, vocab_size, embedding_size, pretrained_embeddings = True, trainable_embeddings = True): - with tf.device('/cpu:0'), tf.name_scope("embedding"): + if pretrained_embeddings: + embedding_placeholder = tf.placeholder(tf.float32, shape=[vocab_size, embedding_size]) + lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), + trainable=trainable_embeddings, name="W") + embedding_init = tf.assign(lookup_table, embedding_placeholder) + + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) + return embedded_words, embedding_init, embedding_placeholder + + else: + lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), + name="W") + embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) + return embedded_words - if pretrained_embeddings: - embedding_placeholder = tf.placeholder(tf.float32, shape=[vocab_size, embedding_size]) - lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), - trainable=trainable_embeddings, name="W") - embedding_init = tf.assign(lookup_table, embedding_placeholder) - - embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) - return embedded_words, embedding_init, embedding_placeholder - - else: - lookup_table = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), - name="W") - embedded_words = tf.nn.embedding_lookup(lookup_table, x_tensor) - return embedded_words - def _extract_vocab_size(X): # max index of vocabulary is encoded in last column From 6867be023c5487756555455bb44d888a9b726076 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 28 Aug 2017 12:12:27 +0200 Subject: [PATCH 068/116] Specify number of steps / weight updates before doing evaluation --- .../lucid_ml/classifying/tensorflow_models.py | 29 ++++++++++++++++--- Code/lucid_ml/run.py | 12 ++++++-- 2 files changed, 34 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 3ec4375..a30deed 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -370,7 +370,8 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre validation_metric = lambda y1, y2 : f1_score(y1, y2, average = "samples"), optimize_threshold = True, threshold_window = np.linspace(-0.03, 0.03, num=7), - tf_model_path = ".tmp_best_models"): + tf_model_path = ".tmp_best_models", + num_steps_before_validation = None): """ """ @@ -379,6 +380,7 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre # enable early stopping on validation set self.validation_data_position = None + self.num_steps_before_validation = num_steps_before_validation # used by this class self.validation_metric = validation_metric @@ -433,6 +435,12 @@ def fit(self, X, y): validation_batch_generator = BatchGenerator(X_val, y_val, self.batch_size, False, False) validation_predictions = self._calc_num_steps(X_val) steps_per_epoch = self._calc_num_steps(X_train) + + # determine after how many batches to perform validation + num_steps_before_validation = self.num_steps_before_validation + if self.num_steps_before_validation is None: + num_steps_before_validation = steps_per_epoch + num_steps_before_validation = int(min(steps_per_epoch, num_steps_before_validation)) else: steps_per_epoch = self._calc_num_steps(X) X_train = X @@ -474,7 +482,10 @@ def fit(self, X, y): best_validation_score = math.inf * objective epochs_of_no_improvement = 0 most_consecutive_epochs_with_no_improvement = 0 - for epoch in range(self.num_epochs): + batches_counter = 0 + epoch = 0 + stop_early = False + while epoch < self.num_epochs and not stop_early: if val_pos is not None and epochs_of_no_improvement == self.patience: break @@ -489,9 +500,16 @@ def fit(self, X, y): # overwrite parameter values for prediction step feed_dict.update(self.params_predict) loss = session.run(self.loss, feed_dict = feed_dict) - + + batches_counter += 1 + is_last_epoch = epoch == self.num_epochs - 1 + is_last_batch_in_epoch = batch_i == steps_per_epoch - 1 # calculate validation loss at end of epoch if early stopping is on - if batch_i + 1 == steps_per_epoch and val_pos is not None: + if (batches_counter == num_steps_before_validation + or (is_last_epoch and is_last_batch_in_epoch)) and val_pos is not None: + + batches_counter = 0 + validation_scores = [] weights = [] @@ -537,12 +555,15 @@ def fit(self, X, y): epochs_of_no_improvement += 1 if epochs_of_no_improvement > self.patience: print("No improvement in validation loss for", self.patience, "epochs. Stopping early.") + stop_early = True break # print progress print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Score: {:0.4f}, Best Validation-Score: {:0.4f}, Threshold: {:0.2f}'. format(epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, loss, avg_validation_score, best_validation_score, self.threshold), end='\r') + epoch += 1 + self.session = session print('') diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 2a3e7de..f92e58b 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -557,7 +557,8 @@ def create_classifier(options, num_concepts): tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, get_model = mlp_base(hidden_activation_function = options.hidden_activation_function), - patience = options.patience), + patience = options.patience, + num_steps_before_validation = options.num_steps_before_validation), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -566,13 +567,15 @@ def create_classifier(options, num_concepts): get_model = mlp_soph(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, self_normalizing = options.snn, standard_normal = options.standard_normal), - patience = options.patience), + patience = options.patience, + num_steps_before_validation = options.num_steps_before_validation), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, patience = options.patience, + num_steps_before_validation = options.num_steps_before_validation, get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, @@ -583,6 +586,7 @@ def create_classifier(options, num_concepts): tf_model_path = options.tf_model_path, optimize_threshold = options.optimize_threshold, patience = options.patience, + num_steps_before_validation = options.num_steps_before_validation, get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, @@ -740,7 +744,9 @@ def _generate_parsers(): classifier_options.add_argument('-e', type=int, dest="max_iterations", default=5, help= "Determine the number of epochs for the training of several classifiers [5]") classifier_options.add_argument('--patience', type=int, dest="patience", default=5, help= - "Specify the number of epochs of no improvement in validation score before training is stopped. [5]") + "Specify the number of steps of no improvement in validation score before training is stopped. [5]") + classifier_options.add_argument('--num_steps_before_validation', type=int, dest="num_steps_before_validation", default=None, help= + "Specify the number of steps before evaluating on the validation set. [None]") classifier_options.add_argument('--learning_rate', type=float, dest="learning_rate", default=None, help= "Determine the learning rate for training of several classifiers. If set to 'None', the learning rate is automatically based on an empirical good value and \ adapted to the batch size. [None]") From 88009c7524b5f3b1acad3b6a81b97c4375b001f5 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 28 Aug 2017 13:16:17 +0200 Subject: [PATCH 069/116] Moved normalization to utils in order to test it --- .../lucid_ml/classifying/tensorflow_models.py | 16 +------ Code/lucid_ml/utils/tf_utils.py | 48 +++++++++++++++++++ 2 files changed, 50 insertions(+), 14 deletions(-) create mode 100644 Code/lucid_ml/utils/tf_utils.py diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 713dae7..3512a15 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -9,6 +9,7 @@ from tensorflow.python.ops import math_ops, random_ops, array_ops from tensorflow.python.layers import utils from datetime import datetime +from utils.tf_utils import tf_normalize #tf.logging.set_verbosity(tf.logging.INFO) def _load_embeddings(filename, vocab_size, embedding_size): @@ -239,20 +240,7 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer # we need to have the input data scaled such they have mean 0 and variance 1 if self_normalizing or standard_normal: - m = X.mean(axis = 0) - - # because we are dealing with sparse matrices, we need to compute the variance as - # E[X^2] - E[X]^2 - X_square = X.power(2) - m_square = X_square.mean(axis = 0) - v = m_square - np.power(m, 2) - s = np.sqrt(v) - - - m = tf.constant(m, dtype = tf.float32) - s = tf.constant(s, dtype = tf.float32) - - scaled_input = (x_tensor - m) / s + scaled_input = tf_normalize(X, x_tensor) else: scaled_input = x_tensor diff --git a/Code/lucid_ml/utils/tf_utils.py b/Code/lucid_ml/utils/tf_utils.py new file mode 100644 index 0000000..b7c0713 --- /dev/null +++ b/Code/lucid_ml/utils/tf_utils.py @@ -0,0 +1,48 @@ +import tensorflow as tf +import numpy as np +from scipy.sparse import csr_matrix + +def tf_normalize(X, input_tensor): + """ + Given data as a sparse matrix X, this function scales an input tensor such that each + column in the output tensor has mean 0 and variance 1. + + >>> X = np.array([[0, 0, 0, 0, 1, 1, 1, 2, 3], + ... [1, 1, 0, 2, 3, 4, 5, 1, 0], + ... [0, 0 ,4, 5, -1, 4, 2, 1, 0]]) + >>> means = np.mean(X, axis = 0) + >>> stds = np.std(X, axis = 0) + >>> scaled_X = (X - means) / stds + >>> X = csr_matrix(X) + >>> input_tensor = tf.placeholder(dtype = tf.float32, shape = [3, 9]) + >>> output_tensor = tf_normalize(X, input_tensor) + >>> sess = tf.Session() + >>> output = sess.run(output_tensor, feed_dict = {input_tensor : X.toarray()}) + >>> output + array([[-0.70710683, -0.70710683, -0.70710683, -1.13555002, 0. , + -1.41421354, -0.98058075, 1.41421342, 1.41421354], + [ 1.41421342, 1.41421342, -0.70710683, -0.16222139, 1.2247448 , + 0.70710677, 1.37281287, -0.70710689, -0.70710677], + [-0.70710683, -0.70710683, 1.41421342, 1.29777145, -1.2247448 , + 0.70710677, -0.39223233, -0.70710689, -0.70710677]], dtype=float32) + """ + m = X.mean(axis = 0) + + # because we are dealing with sparse matrices, we need to compute the variance as + # E[X^2] - E[X]^2 + X_square = X.power(2) + m_square = X_square.mean(axis = 0) + v = m_square - np.power(m, 2) + s = np.sqrt(v) + + + m = tf.constant(m, dtype = tf.float32) + s = tf.constant(s, dtype = tf.float32) + + scaled_input = (input_tensor - m) / s + + return scaled_input + +if __name__ == "__main__": + import doctest + doctest.testmod() \ No newline at end of file From 64120b9c801049f65c9b0a9a215efd222884fc14 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 28 Aug 2017 13:55:47 +0200 Subject: [PATCH 070/116] Fixed division by zero when scaling --- Code/lucid_ml/utils/tf_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Code/lucid_ml/utils/tf_utils.py b/Code/lucid_ml/utils/tf_utils.py index b7c0713..3fe4657 100644 --- a/Code/lucid_ml/utils/tf_utils.py +++ b/Code/lucid_ml/utils/tf_utils.py @@ -35,6 +35,8 @@ def tf_normalize(X, input_tensor): v = m_square - np.power(m, 2) s = np.sqrt(v) + #make sure not to divide by zero when scaling + s[s == 0] = 1 m = tf.constant(m, dtype = tf.float32) s = tf.constant(s, dtype = tf.float32) From 7a55bb25a70cdc5696af4db7ef79785d2423f293 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 28 Aug 2017 14:15:12 +0200 Subject: [PATCH 071/116] Character n-grams as features --- Code/lucid_ml/run.py | 11 ++++++++++- Code/lucid_ml/utils/nltk_normalization.py | 1 + 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index f92e58b..08138c9 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -48,7 +48,7 @@ from classifying.tensorflow_models import MultiLabelSKFlow, mlp_base, mlp_soph, cnn, lstm from utils.Extractor import load_dataset from utils.metrics import hierarchical_f_measure, f1_per_sample -from utils.nltk_normalization import NltkNormalizer, word_regexp +from utils.nltk_normalization import NltkNormalizer, word_regexp, character_regexp from utils.persister import Persister from weighting.SpreadingActivation import SpreadingActivation, BinarySA, OneHopActivation from weighting.synset_analysis import SynsetAnalyzer @@ -109,6 +109,11 @@ def _build_features(options): terms = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, token_pattern=word_regexp, max_features=options.max_features, ngram_range=(1, options.ngram_limit)) + if options.charngrams: + character_ngrams = CountVectorizer(input=input_format, binary=options.binary, + token_pattern=character_regexp, max_features=options.max_features, + ngram_range=(1, options.ngram_limit), + analyzer = 'char_wb') if options.hierarchical: hierarchy = tr.nx_graph @@ -144,6 +149,8 @@ def _build_features(options): extractor = terms elif options.concepts: extractor = concepts + elif options.charngrams: + extractor = character_ngrams elif options.onehot: extractor = TextEncoder(input_format = "filename" if options.fulltext else "content", max_words=options.max_features, pretrained = options.pretrained_embeddings) @@ -700,6 +707,8 @@ def _generate_parsers(): "use concepts [False]") feature_options.add_argument('-t', '--terms', action="store_true", dest="terms", default=False, help= \ "use terms [True]") + feature_options.add_argument('--charngrams', action="store_true", dest="charngrams", default=False, help= \ + "use character n-grams [True]") feature_options.add_argument('--onehot', action="store_true", dest="onehot", default=False, help= \ "Encode the input words as one hot. [True]") feature_options.add_argument('--max_features', type=int, dest="max_features", default=None, help= \ diff --git a/Code/lucid_ml/utils/nltk_normalization.py b/Code/lucid_ml/utils/nltk_normalization.py index 58c84fd..9c5cd82 100644 --- a/Code/lucid_ml/utils/nltk_normalization.py +++ b/Code/lucid_ml/utils/nltk_normalization.py @@ -6,6 +6,7 @@ _alphabet = set(string.ascii_lowercase + string.digits + ' ') word_regexp = r"(?u)\b[a-zA-Z_][a-zA-Z_]+\b" +character_regexp = r"(?u)\b[a-zA-Z_0-9]\b" class NltkNormalizer: def __init__(self): From 7d1454596f2a727bd0a99802d0519c96d9976f5b Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 28 Aug 2017 14:21:43 +0200 Subject: [PATCH 072/116] Option for identity as activation function on hidden layer --- Code/lucid_ml/classifying/tensorflow_models.py | 2 ++ Code/lucid_ml/run.py | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 3512a15..fce5cc5 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -273,6 +273,8 @@ def _transform_activation_function(func): hidden_activation_function = tf.nn.relu elif func == "tanh": hidden_activation_function = tf.nn.tanh + elif func == "identity": + hidden_activation_function = tf.identity return hidden_activation_function def mlp_base(hidden_activation_function = "relu"): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 08138c9..78ebc9c 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -786,7 +786,7 @@ def _generate_parsers(): followed by the entries of its vectors, separated by blanks. If None is specified, the word embeddings are zero-initialized and trained\ jointly with the classification task. [None]") neural_network_options.add_argument('--hidden_activation_function', type=str, dest="hidden_activation_function", default="relu", help= - "Specify the activation function used on the hidden layers in MLP-Base and MLP-Soph. [relu]", choices = ["relu", "tanh"]) + "Specify the activation function used on the hidden layers in MLP-Base and MLP-Soph. [relu]", choices = ["relu", "tanh", "identity"]) neural_network_options.add_argument('--trainable_embeddings', action="store_true", dest="trainable_embeddings", default=False, help= "Whether to keep training the pretrained embeddings further with classification the task or not. [False]") neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= @@ -795,7 +795,7 @@ def _generate_parsers(): neural_network_options.add_argument('--standard_normal', action="store_true", dest="standard_normal", default=False, help= "Whether to normalize the input features to mean = 0 and std = 1 for MLPSoph. [False]") neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= - "Whether to use SELU activation and -dropout. If set to False, RELU activation is used. [False]") + "Whether to use SELU activation and -dropout. If set to False, the activation specified in --hidden_activation_function is used. [False]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") From 0229b6496a857c5abd5bdf96c2ccabc8c1e672e0 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 30 Aug 2017 14:44:22 +0200 Subject: [PATCH 073/116] Fixed a bug where tf models crash without validation set --- Code/lucid_ml/classifying/tensorflow_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index fce5cc5..5f06717 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -493,8 +493,8 @@ def fit(self, X, y): is_last_epoch = epoch == self.num_epochs - 1 is_last_batch_in_epoch = batch_i == steps_per_epoch - 1 # calculate validation loss at end of epoch if early stopping is on - if (batches_counter == num_steps_before_validation - or (is_last_epoch and is_last_batch_in_epoch)) and val_pos is not None: + if val_pos is not None and (batches_counter == num_steps_before_validation + or (is_last_epoch and is_last_batch_in_epoch)): batches_counter = 0 From 5455b7ad29b91ebbf62d9339120cbde8fffa9a67 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 30 Aug 2017 15:22:03 +0200 Subject: [PATCH 074/116] Max features applies to each ngram group individually --- Code/lucid_ml/run.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 78ebc9c..b3b23ee 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -106,9 +106,20 @@ def _build_features(options): concepts = CountVectorizer(input=input_format, analyzer=concept_analyzer, binary=options.binary, vocabulary=tr.nodename_index if not options.synsets else None, ngram_range=(1, options.ngram_limit)) - terms = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, - token_pattern=word_regexp, max_features=options.max_features, - ngram_range=(1, options.ngram_limit)) + + # pick max_features from each ngram-group + if options.max_features is not None and options.ngram_limit > 1: + ngram_vectorizers = [] + for i in range(1, options.ngram_limit + 1): + i_grams = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, + token_pattern=word_regexp, max_features=options.max_features, + ngram_range=(i, i)) + ngram_vectorizers.append(i_grams) + terms = FeatureUnion([(str(i) + "_gram", t) for i, t in enumerate(ngram_vectorizers)]) + else: + terms = CountVectorizer(input=input_format, stop_words='english', binary=options.binary, + token_pattern=word_regexp, max_features=options.max_features, + ngram_range=(1, options.ngram_limit)) if options.charngrams: character_ngrams = CountVectorizer(input=input_format, binary=options.binary, token_pattern=character_regexp, max_features=options.max_features, From b1a1cf968aecd6d5e73e14e57b4b65294d723245 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 5 Sep 2017 14:49:12 +0200 Subject: [PATCH 075/116] Restrict word embedding lookup table to only those that occur in the training set. --- .../lucid_ml/classifying/tensorflow_models.py | 4 +- Code/lucid_ml/utils/text_encoding.py | 80 ++++++++++++------- 2 files changed, 52 insertions(+), 32 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 5f06717..c08bdb3 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -15,9 +15,7 @@ def _load_embeddings(filename, vocab_size, embedding_size): embeddings = np.zeros((vocab_size, embedding_size)) - with open(filename,'r') as embedding_file: - # skip first line, which we dont need - embedding_file.readline() + with open(filename + ".tmp",'r') as embedding_file: i = 0 for line in embedding_file.readlines(): diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index e737bc1..be1ff6f 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -9,12 +9,18 @@ class TextEncoder(BaseEstimator, TransformerMixin): - def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None, pretrained = None): + def __init__(self, + tokenize = NltkNormalizer().split_and_normalize, + input_format = "content", + max_words = None, + pretrained = None, + restrict_pretrained = True): self.tokenize = tokenize self.input = input_format self.max_words = max_words self.pretrained = pretrained + self.restrict_pretrained = restrict_pretrained def _maybe_load_text(self, text): if self.input == "filename": @@ -30,24 +36,45 @@ def _limit_num_words(self, words, max_length): return words @staticmethod - def _load_pretrained_vocabulary(filename): + def _load_pretrained_vocabulary(filename, word_restrictions): mapping = {} - with open(filename,'r') as embedding_file: - embedding_size = int(embedding_file.readline().strip().split(" ")[1]) - - i = 0 - for line in embedding_file.readlines(): - row = line.strip().split(' ') - - # make sure we dont use escape sequences and so on - if len(row) == embedding_size + 1: - if row[0] in mapping: - print(row[0], "is already in mapping") - else: - mapping[row[0]] = i - i += 1 + + with open(filename + ".tmp", 'w') as temp_embedding_file: + + with open(filename,'r') as embedding_file: + embedding_size = int(embedding_file.readline().strip().split(" ")[1]) + + i = 0 + for line in embedding_file.readlines(): + row = line.strip().split(' ') + + # make sure we dont use escape sequences and so on + if len(row) == embedding_size + 1: + if row[0] in mapping: + print(row[0], "is already in mapping") + elif word_restrictions is None or row[0] in word_restrictions: + mapping[row[0]] = i + i += 1 + temp_embedding_file.write(line) return mapping, i + def _extract_words(self, text): + # if full-text: load it first + text = self._maybe_load_text(text) + + # tokenize training text + words = self.tokenize(text) + words = self._limit_num_words(words, self.max_words) + + return words + + def _set_of_all_words(self, X): + all_words = set() + for text in X: + all_words.update(set(self._extract_words(text))) + + return all_words + def fit(self, X, y = None): """ X is expected to be a list of either strings, which represent the text directly, or paths to the @@ -60,12 +87,7 @@ def fit(self, X, y = None): max_length = 0 for text in X: - # if full-text: load it first - text = self._maybe_load_text(text) - - # tokenize training text - words = self.tokenize(text) - words = self._limit_num_words(words, self.max_words) + words = self._extract_words(text) # build mapping from word to index @@ -81,16 +103,16 @@ def fit(self, X, y = None): else: - mapping, max_index = TextEncoder._load_pretrained_vocabulary(self.pretrained) + if self.restrict_pretrained: + all_words = self._set_of_all_words(X) + else: + all_words = None + + mapping, max_index = TextEncoder._load_pretrained_vocabulary(self.pretrained, all_words) max_length = 0 for text in X: - # if full-text: load it first - text = self._maybe_load_text(text) - - # tokenize training text and look for the maximum - words = self.tokenize(text) - words = self._limit_num_words(words, self.max_words) + words = self._extract_words(text) if len(words) > max_length: max_length = len(words) From d64a4693a533d20c5a3625fc320e6e23c2d6a4c2 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 29 Sep 2017 17:07:14 +0200 Subject: [PATCH 076/116] Bottleneck layer --- .../lucid_ml/classifying/tensorflow_models.py | 20 +++++++++++++++++- Code/lucid_ml/run.py | 21 +++++++++++++++---- 2 files changed, 36 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index c08bdb3..e95b339 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -357,7 +357,10 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre optimize_threshold = True, threshold_window = np.linspace(-0.03, 0.03, num=7), tf_model_path = ".tmp_best_models", - num_steps_before_validation = None): + num_steps_before_validation = None, + hidden_activation_function = tf.nn.relu, + bottleneck_layers = None, + hidden_keep_prob = 0.5): """ """ @@ -368,6 +371,11 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre self.validation_data_position = None self.num_steps_before_validation = num_steps_before_validation + # configurations for bottleneck layers + self.hidden_activation_function = hidden_activation_function + self.bottleneck_layers = bottleneck_layers + self.hidden_keep_prob = hidden_keep_prob + # used by this class self.validation_metric = validation_metric self.optimize_threshold = optimize_threshold @@ -441,6 +449,16 @@ def fit(self, X, y): # get_model has to return a self.x_tensor, self.y_tensor, self.last_layer, self.params_fit, self.params_predict, initializer_operations = self.get_model(X, y) + # add bottleneck layer + if self.bottleneck_layers is not None: + bottleneck_dropout_tensor = tf.placeholder(tf.float32, name = "bottleneck_dropout") + self.params_fit.update({bottleneck_dropout_tensor : self.hidden_keep_prob}) + self.params_predict.update({bottleneck_dropout_tensor : 1}) + for units in self.bottleneck_layers: + self.last_layer = tf.contrib.layers.fully_connected(self.last_layer, units, activation_fn = self.hidden_activation_function) + self.last_layer = tf.nn.dropout(self.last_layer, bottleneck_dropout_tensor) + + # Name logits Tensor, so that is can be loaded from disk after training #logits = tf.identity(logits, name='logits') logits = tf.contrib.layers.linear(self.last_layer, diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index b3b23ee..42d5a86 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -576,7 +576,9 @@ def create_classifier(options, num_concepts): optimize_threshold = options.optimize_threshold, get_model = mlp_base(hidden_activation_function = options.hidden_activation_function), patience = options.patience, - num_steps_before_validation = options.num_steps_before_validation), + num_steps_before_validation = options.num_steps_before_validation, + bottleneck_layers = options.bottleneck_layers, + hidden_keep_prob = options.dropout), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -586,7 +588,9 @@ def create_classifier(options, num_concepts): hidden_layers = options.hidden_layers, self_normalizing = options.snn, standard_normal = options.standard_normal), patience = options.patience, - num_steps_before_validation = options.num_steps_before_validation), + num_steps_before_validation = options.num_steps_before_validation, + bottleneck_layers = options.bottleneck_layers, + hidden_keep_prob = options.dropout), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -597,7 +601,9 @@ def create_classifier(options, num_concepts): get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, - trainable_embeddings=options.trainable_embeddings)), + trainable_embeddings=options.trainable_embeddings), + bottleneck_layers = options.bottleneck_layers, + hidden_keep_prob = options.dropout), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -608,7 +614,9 @@ def create_classifier(options, num_concepts): get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, - trainable_embeddings = options.trainable_embeddings)), + trainable_embeddings = options.trainable_embeddings), + bottleneck_layers = options.bottleneck_layers, + hidden_keep_prob = options.dropout), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -803,6 +811,11 @@ def _generate_parsers(): neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= "Specify the number of layers and the respective number of units as a list. The i-th element of the list \ specifies the number of units in layer i. [1000]") + neural_network_options.add_argument('--bottleneck_layers', type=int, dest="bottleneck_layers", nargs='+', default=None, help= + "Specify the number of bottleneck layers and the respective number of units as a list. The i-th element of the list \ + specifies the number of units in layer i. In contrast to the --hidden_layers option, where the respective model decides\ + how to interprete multiple hidden layers, the bottleneck layers are feed forward layers which are pluged in between \ + the last layer of a particular model (e.g. CNN, LSTM) and the output layer. (None)") neural_network_options.add_argument('--standard_normal', action="store_true", dest="standard_normal", default=False, help= "Whether to normalize the input features to mean = 0 and std = 1 for MLPSoph. [False]") neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= From f60e22d3582cb0ef3e547fe3fa1b390021a25f60 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 29 Sep 2017 17:14:29 +0200 Subject: [PATCH 077/116] Dynamic max pooling --- .../lucid_ml/classifying/tensorflow_models.py | 36 +++++++++++++------ Code/lucid_ml/run.py | 5 ++- 2 files changed, 30 insertions(+), 11 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index e95b339..3d7f7c5 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -111,7 +111,8 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, initializer_operations def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], pretrained_embeddings_path = None, - trainable_embeddings = True): + trainable_embeddings = True, + dynamic_max_pooling_p = 1): """Model function for CNN.""" # x_tensor includes the max_index_column, feature_input doesnt. go on with feature_input, but return x_tensor for feed_dict @@ -146,15 +147,29 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ conv = tf.nn.conv2d(embedded_words, filter_weights, stride, padding) bias = tf.Variable(tf.random_normal([num_filters])) detector = tf.nn.relu(tf.nn.bias_add(conv, bias)) - pooling = tf.nn.max_pool(detector, - ksize = [1, sequence_length - window_size + 1, 1, 1], - strides = stride, - padding = "VALID") - pooled_outputs.append(tf.reshape(pooling, [-1, num_filters])) + map_size = detector.get_shape().as_list()[1] + + # dynamic max-pooling: extract maximum for each chunk + chunks_size = int(np.ceil(map_size / dynamic_max_pooling_p)) + chunk_poolings = [] + for i in range(dynamic_max_pooling_p): + + # make sure we don't get out of bounds at end of sequence + cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 else map_size % dynamic_max_pooling_p + + tf.slice(detector, [0, i * chunks_size, 0, 0], [-1, cur_chunk_size, 1, num_filters]) + pooling = tf.nn.max_pool(detector, + ksize = [1, sequence_length - window_size + 1, 1, 1], + strides = stride, + padding = "VALID") + pooling = tf.reshape(pooling, [-1, num_filters]) + chunk_poolings.append(pooling) + + concatenated_pooled_chunks = tf.concat(chunk_poolings, 1) + pooled_outputs.append(concatenated_pooled_chunks) - concatenated_pools = tf.concat(pooled_outputs, 1) - num_filters_total = num_filters * len(window_sizes) + num_filters_total = num_filters * len(window_sizes) * dynamic_max_pooling_p hidden_layer = tf.reshape(concatenated_pools, [-1, num_filters_total]) @@ -291,11 +306,12 @@ def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, hidden_activation_function=hidden_activation_function, standard_normal = standard_normal) -def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): +def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, dynamic_max_pooling_p): return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, - trainable_embeddings = trainable_embeddings) + trainable_embeddings = trainable_embeddings, + dynamic_max_pooling_p = dynamic_max_pooling_p) def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 42d5a86..90eb824 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -601,7 +601,8 @@ def create_classifier(options, num_concepts): get_model = cnn(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, - trainable_embeddings=options.trainable_embeddings), + trainable_embeddings=options.trainable_embeddings, + dynamic_max_pooling_p=options.dynamic_max_pooling_p), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, @@ -822,6 +823,8 @@ def _generate_parsers(): "Whether to use SELU activation and -dropout. If set to False, the activation specified in --hidden_activation_function is used. [False]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") + neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= + "Specify the number of chunks (p) to perform max-pooling over. [1]") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 2c0de053349b484e0074078a365b1b3454c4063e Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 29 Sep 2017 18:41:34 +0200 Subject: [PATCH 078/116] Variational recurrent dropout for LSTM --- .../lucid_ml/classifying/tensorflow_models.py | 22 +++++++++++++++---- Code/lucid_ml/run.py | 5 ++++- 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 3d7f7c5..2b5c482 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -75,7 +75,8 @@ def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_s def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = True, pretrained_embeddings_path = None, - trainable_embeddings = True): + trainable_embeddings = True, + variational_recurrent_dropout = True): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -96,7 +97,19 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = initializer_operations) # build multiple layers of lstms - stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) for hidden_layer_size in hidden_layers]) + lstm_layers = [] + for hidden_layer_size in hidden_layers: + single_lstm_layer = tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) + if variational_recurrent_dropout: + single_lstm_layer = tf.contrib.rnn.DropoutWrapper(single_lstm_layer, + input_keep_prob=1.0, + output_keep_prob=1.0, + state_keep_prob=dropout_tensor, + variational_recurrent=True, + input_size = embedding_size, + dtype = tf.float32) + lstm_layers.append(single_lstm_layer) + stacked_lstm = tf.contrib.rnn.MultiRNNCell(lstm_layers) state = stacked_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) #state = tf.zeros([tf.shape(embedded_words)[0].value, stacked_lstm.state_size]) @@ -313,11 +326,12 @@ def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_ trainable_embeddings = trainable_embeddings, dynamic_max_pooling_p = dynamic_max_pooling_p) -def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings): +def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout): return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, - trainable_embeddings = trainable_embeddings) + trainable_embeddings = trainable_embeddings, + variational_recurrent_dropout=variational_recurrent_dropout) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 90eb824..be4b9c3 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -615,7 +615,8 @@ def create_classifier(options, num_concepts): get_model = lstm(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, - trainable_embeddings = options.trainable_embeddings), + trainable_embeddings = options.trainable_embeddings, + variational_recurrent_dropout = options.variational_recurrent_dropout), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, @@ -821,6 +822,8 @@ def _generate_parsers(): "Whether to normalize the input features to mean = 0 and std = 1 for MLPSoph. [False]") neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= "Whether to use SELU activation and -dropout. If set to False, the activation specified in --hidden_activation_function is used. [False]") + neural_network_options.add_argument('--variational_recurrent_dropout', action="store_true", dest="variational_recurrent_dropout", default=False, help= + "Whether to perform dropout on the recurrent unit between states in addition to dropout on the aggregated output. [False]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= From 40f6d9bf8796706e98bbe0a1ebcd7238f753fdbf Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 29 Sep 2017 18:57:14 +0200 Subject: [PATCH 079/116] Option for setting fraction of GPU memory to use --- Code/lucid_ml/classifying/tensorflow_models.py | 9 +++++++-- Code/lucid_ml/run.py | 14 ++++++++++---- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 2b5c482..6d40f0d 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -390,7 +390,8 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre num_steps_before_validation = None, hidden_activation_function = tf.nn.relu, bottleneck_layers = None, - hidden_keep_prob = 0.5): + hidden_keep_prob = 0.5, + gpu_memory_fraction = 1.): """ """ @@ -423,6 +424,9 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre self.TF_MODEL_PATH = tf_model_path self._save_model_path = self._get_save_model_path() + # determine how much of gpu to use + self.gpu_memory_fraction = gpu_memory_fraction + def _get_save_model_path(self): TMP_FOLDER = self.TF_MODEL_PATH if not os.path.exists(TMP_FOLDER): @@ -503,7 +507,8 @@ def fit(self, X, y): # prediction self.predictions = tf.sigmoid(logits) - session = tf.Session() + gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.gpu_memory_fraction) + session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) # Initializing the variables session.run(tf.global_variables_initializer()) for (init_op, init_op_feed_dict) in initializer_operations: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index be4b9c3..a01eda1 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -578,7 +578,8 @@ def create_classifier(options, num_concepts): patience = options.patience, num_steps_before_validation = options.num_steps_before_validation, bottleneck_layers = options.bottleneck_layers, - hidden_keep_prob = options.dropout), + hidden_keep_prob = options.dropout, + gpu_memory_fraction = options.memory), "mlpsoph" : MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -590,7 +591,8 @@ def create_classifier(options, num_concepts): patience = options.patience, num_steps_before_validation = options.num_steps_before_validation, bottleneck_layers = options.bottleneck_layers, - hidden_keep_prob = options.dropout), + hidden_keep_prob = options.dropout, + gpu_memory_fraction = options.memory), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -604,7 +606,8 @@ def create_classifier(options, num_concepts): trainable_embeddings=options.trainable_embeddings, dynamic_max_pooling_p=options.dynamic_max_pooling_p), bottleneck_layers = options.bottleneck_layers, - hidden_keep_prob = options.dropout), + hidden_keep_prob = options.dropout, + gpu_memory_fraction = options.memory), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -618,7 +621,8 @@ def create_classifier(options, num_concepts): trainable_embeddings = options.trainable_embeddings, variational_recurrent_dropout = options.variational_recurrent_dropout), bottleneck_layers = options.bottleneck_layers, - hidden_keep_prob = options.dropout), + hidden_keep_prob = options.dropout, + gpu_memory_fraction = options.memory), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -799,6 +803,8 @@ def _generate_parsers(): neural_network_options = parser.add_argument_group("Neural Network Options") neural_network_options.add_argument('--dropout', type=float, dest="dropout", default=0.5, help= "Determine the keep probability for all dropout layers.") + neural_network_options.add_argument('--memory', type=float, dest="memory", default=1.0, help= + "Fraction of available GPU-memory to use for experiment.") neural_network_options.add_argument('--embedding_size', type=int, dest="embedding_size", default=300, help= "Determine the size of a word embedding vector (for MLP-Soph, CNN, and LSTM if embedding is learned jointly). \ Specify --embedding_size=0 to skip the embedding layer, if applicable. [300]") From 901b5c0cb196f7af50b8de891e3b395f6da6cc5f Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 8 Oct 2017 15:13:38 +0200 Subject: [PATCH 080/116] Specify multiple feature extraction methods as parameters, merge all --- Code/lucid_ml/run.py | 35 +++++++++++++++++++---------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index a01eda1..062c975 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -123,7 +123,7 @@ def _build_features(options): if options.charngrams: character_ngrams = CountVectorizer(input=input_format, binary=options.binary, token_pattern=character_regexp, max_features=options.max_features, - ngram_range=(1, options.ngram_limit), + ngram_range=(1, options.char_ngram_limit), analyzer = 'char_wb') if options.hierarchical: @@ -151,22 +151,23 @@ def _build_features(options): activation = SpreadingActivation(tr.nx_graph, firing_threshold=1.0, decay=0.25, weighting=None) concepts = make_pipeline(concepts, activation) + features = [] if options.graph_scoring_method: - extractor = GraphVectorizer(method=options.graph_scoring_method, analyzer=concept_analyzer - if options.concepts else NltkNormalizer().split_and_normalize) - elif options.terms and (options.concepts or options.synsets): - extractor = FeatureUnion([("terms", terms), ("concepts", concepts)]) - elif options.terms: - extractor = terms - elif options.concepts: - extractor = concepts - elif options.charngrams: - extractor = character_ngrams - elif options.onehot: - extractor = TextEncoder(input_format = "filename" if options.fulltext else "content", - max_words=options.max_features, pretrained = options.pretrained_embeddings) - else: + features.append(("graph_vectorizer", GraphVectorizer(method=options.graph_scoring_method, analyzer=concept_analyzer + if options.concepts else NltkNormalizer().split_and_normalize))) + if options.terms: + features.append(("terms", terms)) + if options.concepts: + features.append(("concets", concepts)) + if options.charngrams: + features.append(("char_ngrams", character_ngrams)) + if options.onehot: + features.append(("onehot", TextEncoder(input_format = "filename" if options.fulltext else "content", + max_words=options.max_features, pretrained = options.pretrained_embeddings))) + if len(features) == 0: raise ValueError("No feature representation specified!") + + extractor = FeatureUnion(features) if VERBOSE: print("Extracting features...") if VERBOSE > 1: start_ef = default_timer() @@ -756,7 +757,9 @@ def _generate_parsers(): feature_options.add_argument('--no-norm', action="store_false", dest="norm", default=True, help="Do not normalize values") feature_options.add_argument('--ngram_limit', type=int, dest="ngram_limit", default=1, help= \ - "Specify the n for n-grams to take into account for BoW vectorization. [1]") + "Specify the n for n-grams to take into account for token-based BoW vectorization. [1]") + feature_options.add_argument('--char_ngram_limit', type=int, dest="char_ngram_limit", default=3, help= \ + "Specify the n for character n-grams to take into account for character n-gram based BoW vectorization. [3]") # group for classifiers classifier_options = parser.add_argument_group("Classifier Options") From 7939b5b14427e7c942a89127eb9a245d3aac86a1 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 8 Oct 2017 15:32:47 +0200 Subject: [PATCH 081/116] Apply variational recurrent dropout only at inner states --- Code/lucid_ml/classifying/tensorflow_models.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 6d40f0d..b2b5008 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -102,11 +102,10 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = single_lstm_layer = tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) if variational_recurrent_dropout: single_lstm_layer = tf.contrib.rnn.DropoutWrapper(single_lstm_layer, - input_keep_prob=1.0, - output_keep_prob=1.0, + input_keep_prob=1., + output_keep_prob=1., state_keep_prob=dropout_tensor, variational_recurrent=True, - input_size = embedding_size, dtype = tf.float32) lstm_layers.append(single_lstm_layer) stacked_lstm = tf.contrib.rnn.MultiRNNCell(lstm_layers) From 7c06abf99f6a7ed52ecbbe10bb5113973e05cc8c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 10 Oct 2017 15:04:40 +0200 Subject: [PATCH 082/116] Pass activation function on hidden layer to MLP soph as well --- Code/lucid_ml/run.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 062c975..37a9719 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -588,7 +588,9 @@ def create_classifier(options, num_concepts): optimize_threshold = options.optimize_threshold, get_model = mlp_soph(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, self_normalizing = options.snn, - standard_normal = options.standard_normal), + standard_normal = options.standard_normal, + hidden_activation_function = options.hidden_activation_function + ), patience = options.patience, num_steps_before_validation = options.num_steps_before_validation, bottleneck_layers = options.bottleneck_layers, From 8ee92eb6c3a546aaae23020b32facde334d23183 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 10 Oct 2017 16:12:24 +0200 Subject: [PATCH 083/116] Option for bidirectional LSTM by creating a (multi-layered) LSTM for reading forward and backward each --- .../lucid_ml/classifying/tensorflow_models.py | 58 ++++++++++++------- Code/lucid_ml/run.py | 6 +- 2 files changed, 42 insertions(+), 22 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index b2b5008..cb3c6a3 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -76,7 +76,8 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = aggregate_output = True, pretrained_embeddings_path = None, trainable_embeddings = True, - variational_recurrent_dropout = True): + variational_recurrent_dropout = True, + bidirectional = False): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -96,24 +97,37 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = trainable_embeddings, initializer_operations) - # build multiple layers of lstms - lstm_layers = [] - for hidden_layer_size in hidden_layers: - single_lstm_layer = tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) - if variational_recurrent_dropout: - single_lstm_layer = tf.contrib.rnn.DropoutWrapper(single_lstm_layer, - input_keep_prob=1., - output_keep_prob=1., - state_keep_prob=dropout_tensor, - variational_recurrent=True, - dtype = tf.float32) - lstm_layers.append(single_lstm_layer) - stacked_lstm = tf.contrib.rnn.MultiRNNCell(lstm_layers) - state = stacked_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) - #state = tf.zeros([tf.shape(embedded_words)[0].value, stacked_lstm.state_size]) - - # we can discard the state after the batch is fully processed - output_state, _ = tf.nn.dynamic_rnn(stacked_lstm, embedded_words, initial_state = state) + def create_multilayer_lstm(): + # build multiple layers of lstms + lstm_layers = [] + for hidden_layer_size in hidden_layers: + single_lstm_layer = tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) + if variational_recurrent_dropout: + single_lstm_layer = tf.contrib.rnn.DropoutWrapper(single_lstm_layer, + input_keep_prob=1., + output_keep_prob=1., + state_keep_prob=dropout_tensor, + variational_recurrent=True, + dtype = tf.float32) + lstm_layers.append(single_lstm_layer) + stacked_lstm = tf.contrib.rnn.MultiRNNCell(lstm_layers) + return stacked_lstm + + forward_lstm = create_multilayer_lstm() + forward_state = forward_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) + + # bidirectional lstm? + ## we can discard the state after the batch is fully processed + if not bidirectional: + output_state, _ = tf.nn.dynamic_rnn(forward_lstm, embedded_words, initial_state = forward_state) + else: + backward_lstm = create_multilayer_lstm() + backward_state = backward_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) + bidi_output_states, _ = tf.nn.bidirectional_dynamic_rnn(forward_lstm, backward_lstm, embedded_words, + initial_state_fw = forward_state, initial_state_bw = backward_state) + ## we concatenate the outputs of forward and backward rnn in accordance with Hierarchical Attention Networks + h1, h2 = bidi_output_states + output_state = tf.concat([h1, h2], axis = 2, name = "concat_bidi_output_states") if aggregate_output: output_state = tf.reduce_mean(output_state, axis = 1) @@ -325,12 +339,14 @@ def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_ trainable_embeddings = trainable_embeddings, dynamic_max_pooling_p = dynamic_max_pooling_p) -def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout): +def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout, + bidirectional): return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, trainable_embeddings = trainable_embeddings, - variational_recurrent_dropout=variational_recurrent_dropout) + variational_recurrent_dropout=variational_recurrent_dropout, + bidirectional = bidirectional) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 37a9719..960ec16 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -622,7 +622,8 @@ def create_classifier(options, num_concepts): hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, trainable_embeddings = options.trainable_embeddings, - variational_recurrent_dropout = options.variational_recurrent_dropout), + variational_recurrent_dropout = options.variational_recurrent_dropout, + bidirectional = options.bidirectional), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, gpu_memory_fraction = options.memory), @@ -835,6 +836,9 @@ def _generate_parsers(): "Whether to use SELU activation and -dropout. If set to False, the activation specified in --hidden_activation_function is used. [False]") neural_network_options.add_argument('--variational_recurrent_dropout', action="store_true", dest="variational_recurrent_dropout", default=False, help= "Whether to perform dropout on the recurrent unit between states in addition to dropout on the aggregated output. [False]") + neural_network_options.add_argument('--bidirectional', action="store_true", dest="bidirectional", default=False, help= + "When activated, we create two instances of (potentially multi-layered) LSTMs, where one reads the input from left to right and \ + the other reads it from right to left. [False]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= From d4c6a86ae5bdc76d712d0e44ac24317a7cb768b2 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 10 Oct 2017 16:51:25 +0200 Subject: [PATCH 084/116] Option for LSTM output aggregation strategy --- .../lucid_ml/classifying/tensorflow_models.py | 21 ++++++++++++++----- Code/lucid_ml/run.py | 6 +++++- 2 files changed, 21 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index cb3c6a3..d15d381 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -73,7 +73,7 @@ def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_s return embedded_words, embedding_size def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], - aggregate_output = True, + aggregate_output = "average", pretrained_embeddings_path = None, trainable_embeddings = True, variational_recurrent_dropout = True, @@ -129,9 +129,19 @@ def create_multilayer_lstm(): h1, h2 = bidi_output_states output_state = tf.concat([h1, h2], axis = 2, name = "concat_bidi_output_states") - if aggregate_output: + if aggregate_output == "average": + # average over outputs at all time steps output_state = tf.reduce_mean(output_state, axis = 1) - + elif aggregate_output == "last": + # return output at last time step + sequence_length = output_state.get_shape().as_list()[1] + output_state = tf.slice(output_state, begin = [0, sequence_length - 1, 0], size = [-1, 1, -1]) + + ## reduce to only 2 dimensions + output_state = tf.reduce_mean(output_state, axis = 1) + else: + raise ValueError("Aggregation method not implemented!") + hidden_layer = tf.nn.dropout(output_state, dropout_tensor) return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, initializer_operations @@ -340,13 +350,14 @@ def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_ dynamic_max_pooling_p = dynamic_max_pooling_p) def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout, - bidirectional): + bidirectional, aggregate_output): return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, trainable_embeddings = trainable_embeddings, variational_recurrent_dropout=variational_recurrent_dropout, - bidirectional = bidirectional) + bidirectional = bidirectional, + aggregate_output = aggregate_output) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 960ec16..00bb8f4 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -623,7 +623,8 @@ def create_classifier(options, num_concepts): pretrained_embeddings_path = options.pretrained_embeddings, trainable_embeddings = options.trainable_embeddings, variational_recurrent_dropout = options.variational_recurrent_dropout, - bidirectional = options.bidirectional), + bidirectional = options.bidirectional, + aggregate_output = options.aggregate_output), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, gpu_memory_fraction = options.memory), @@ -839,6 +840,9 @@ def _generate_parsers(): neural_network_options.add_argument('--bidirectional', action="store_true", dest="bidirectional", default=False, help= "When activated, we create two instances of (potentially multi-layered) LSTMs, where one reads the input from left to right and \ the other reads it from right to left. [False]") + detailed_options.add_argument('--aggregate_output', type=str, dest='aggregate_output', default="average", help= + "How to aggregate the outputs of an LSTM. 'last' uses the output at the last time step. 'average' takes the mean over all outputs. [average]", + choices = ["average", "last"]) neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= From 6382ce5b9a093ce050a76a335e55cb1b6e6d8a69 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Wed, 18 Oct 2017 18:36:35 +0200 Subject: [PATCH 085/116] Fixed a deprecated import. --- Code/lucid_ml/utils/metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/utils/metrics.py b/Code/lucid_ml/utils/metrics.py index 0fdd445..af185ae 100644 --- a/Code/lucid_ml/utils/metrics.py +++ b/Code/lucid_ml/utils/metrics.py @@ -6,7 +6,7 @@ import scipy.sparse as sp from scipy.sparse.sputils import isdense from sklearn.metrics import make_scorer -from sklearn.metrics.base import UndefinedMetricWarning +from sklearn.exceptions import UndefinedMetricWarning from sklearn.utils.sparsefuncs import count_nonzero From 45d5f711f2b64c568b880370349f71fa33d9a71a Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 23 Oct 2017 09:34:02 +0200 Subject: [PATCH 086/116] Use 'vanilla' LSTM and fix averaging over outputs and extracting last output. --- .../lucid_ml/classifying/tensorflow_models.py | 38 ++++++++++++++----- 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index d15d381..9a26377 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -72,6 +72,26 @@ def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_s return embedded_words, embedding_size +def sequence_length(sequence): + used = tf.sign(tf.reduce_max(tf.abs(sequence), 2)) + length = tf.reduce_sum(used, 1) + length = tf.cast(length, tf.int32) + return length + +def extract_axis_1(data, ind): + """ + Get specified elements along the first axis of tensor. + :param data: Tensorflow tensor that will be subsetted. + :param ind: Indices to take (one for each element along axis 0 of data). + :return: Subsetted tensor. + """ + + batch_range = tf.range(tf.shape(data)[0]) + indices = tf.stack([batch_range, ind], axis=1) + res = tf.gather_nd(data, indices) + + return res + def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = "average", pretrained_embeddings_path = None, @@ -101,7 +121,7 @@ def create_multilayer_lstm(): # build multiple layers of lstms lstm_layers = [] for hidden_layer_size in hidden_layers: - single_lstm_layer = tf.contrib.rnn.BasicLSTMCell(hidden_layer_size) + single_lstm_layer = tf.contrib.rnn.LSTMCell(hidden_layer_size, use_peepholes = True) if variational_recurrent_dropout: single_lstm_layer = tf.contrib.rnn.DropoutWrapper(single_lstm_layer, input_keep_prob=1., @@ -116,29 +136,29 @@ def create_multilayer_lstm(): forward_lstm = create_multilayer_lstm() forward_state = forward_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) + seq_length = sequence_length(embedded_words) + # bidirectional lstm? ## we can discard the state after the batch is fully processed if not bidirectional: - output_state, _ = tf.nn.dynamic_rnn(forward_lstm, embedded_words, initial_state = forward_state) + output_state, _ = tf.nn.dynamic_rnn(forward_lstm, embedded_words, initial_state = forward_state, sequence_length = seq_length) else: backward_lstm = create_multilayer_lstm() backward_state = backward_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) bidi_output_states, _ = tf.nn.bidirectional_dynamic_rnn(forward_lstm, backward_lstm, embedded_words, - initial_state_fw = forward_state, initial_state_bw = backward_state) + initial_state_fw = forward_state, initial_state_bw = backward_state, + sequence_length = seq_length) ## we concatenate the outputs of forward and backward rnn in accordance with Hierarchical Attention Networks h1, h2 = bidi_output_states output_state = tf.concat([h1, h2], axis = 2, name = "concat_bidi_output_states") if aggregate_output == "average": # average over outputs at all time steps - output_state = tf.reduce_mean(output_state, axis = 1) + output_state = tf.reduce_sum(output_state, axis = 1) + output_state = tf.div(output_state, tf.cast(tf.reshape(seq_length, [-1, 1]), tf.float32)) elif aggregate_output == "last": # return output at last time step - sequence_length = output_state.get_shape().as_list()[1] - output_state = tf.slice(output_state, begin = [0, sequence_length - 1, 0], size = [-1, 1, -1]) - - ## reduce to only 2 dimensions - output_state = tf.reduce_mean(output_state, axis = 1) + output_state = extract_axis_1(output_state, seq_length - 1) else: raise ValueError("Aggregation method not implemented!") From a064ade2a0f801282cae29c77270fc7968f73bd8 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 23 Oct 2017 12:06:08 +0200 Subject: [PATCH 087/116] Fix sequence length computation --- .../lucid_ml/classifying/tensorflow_models.py | 36 +++++++++++++------ 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 9a26377..8bde389 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -73,7 +73,7 @@ def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_s return embedded_words, embedding_size def sequence_length(sequence): - used = tf.sign(tf.reduce_max(tf.abs(sequence), 2)) + used = tf.sign(sequence) length = tf.reduce_sum(used, 1) length = tf.cast(length, tf.int32) return length @@ -107,6 +107,8 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} + + seq_length = sequence_length(feature_input) initializer_operations = [] @@ -136,8 +138,6 @@ def create_multilayer_lstm(): forward_lstm = create_multilayer_lstm() forward_state = forward_lstm.zero_state(tf.shape(embedded_words)[0], tf.float32) - seq_length = sequence_length(embedded_words) - # bidirectional lstm? ## we can discard the state after the batch is fully processed if not bidirectional: @@ -173,13 +173,16 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ # x_tensor includes the max_index_column, feature_input doesnt. go on with feature_input, but return x_tensor for feed_dict x_tensor, vocab_size, feature_input = _extract_vocab_size(X) - sequence_length = X.shape[1] - 1 + max_length = X.shape[1] - 1 y_tensor = tf.placeholder(tf.float32, shape=(None, y.shape[1]), name = "y") dropout_tensor = tf.placeholder(tf.float32, name = "dropout") params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} + + seq_length = sequence_length(feature_input) + seq_length = tf.cast(seq_length, tf.float32) initializer_operations = [] embedded_words, embedding_size = _init_embedding_layer(pretrained_embeddings_path, @@ -203,19 +206,31 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ conv = tf.nn.conv2d(embedded_words, filter_weights, stride, padding) bias = tf.Variable(tf.random_normal([num_filters])) detector = tf.nn.relu(tf.nn.bias_add(conv, bias)) - map_size = detector.get_shape().as_list()[1] + #map_size = detector.get_shape().as_list()[1] # dynamic max-pooling: extract maximum for each chunk - chunks_size = int(np.ceil(map_size / dynamic_max_pooling_p)) + chunks_size = tf.ceil(tf.divide(seq_length, dynamic_max_pooling_p)) chunk_poolings = [] for i in range(dynamic_max_pooling_p): # make sure we don't get out of bounds at end of sequence - cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 else map_size % dynamic_max_pooling_p + cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 or dynamic_max_pooling_p == 1 else tf.mod(seq_length, dynamic_max_pooling_p) - tf.slice(detector, [0, i * chunks_size, 0, 0], [-1, cur_chunk_size, 1, num_filters]) - pooling = tf.nn.max_pool(detector, - ksize = [1, sequence_length - window_size + 1, 1, 1], + # create a mask for the entire sequence where only those are selected which are in the current chunk + start_indices = i * chunks_size + end_indices = i * chunks_size + cur_chunk_size + end_indices = tf.Print(end_indices, [chunks_size, cur_chunk_size, start_indices, end_indices], message = "Chunking:") + + neg_mask_start = tf.cast(tf.sequence_mask(start_indices, maxlen = max_length - window_size + 1), tf.float32) + mask_end = tf.cast(tf.sequence_mask(end_indices, maxlen = max_length - window_size + 1), tf.float32) + final_mask = tf.multiply(neg_mask_start, mask_end) + final_mask = tf.expand_dims(final_mask, axis = 2) + final_mask = tf.expand_dims(final_mask, axis = 3) + + extracted_chunk = tf.multiply(final_mask, detector) + #tf.slice(detector, [0, i * chunks_size, 0, 0], [-1, cur_chunk_size, 1, num_filters]) + pooling = tf.nn.max_pool(extracted_chunk, + ksize = [1, max_length - window_size + 1, 1, 1], strides = stride, padding = "VALID") pooling = tf.reshape(pooling, [-1, num_filters]) @@ -717,3 +732,4 @@ def predict(self, X): # close the session, since no longer needed session.close() return result + From 0a4d56baf6f68704cd48a3d9d0cf2a98fbaf1aaf Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 23 Oct 2017 13:52:48 +0200 Subject: [PATCH 088/116] Fix dynamic max pooling --- Code/lucid_ml/classifying/tensorflow_models.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 8bde389..d4c4c08 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -206,7 +206,6 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ conv = tf.nn.conv2d(embedded_words, filter_weights, stride, padding) bias = tf.Variable(tf.random_normal([num_filters])) detector = tf.nn.relu(tf.nn.bias_add(conv, bias)) - #map_size = detector.get_shape().as_list()[1] # dynamic max-pooling: extract maximum for each chunk chunks_size = tf.ceil(tf.divide(seq_length, dynamic_max_pooling_p)) @@ -219,16 +218,14 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ # create a mask for the entire sequence where only those are selected which are in the current chunk start_indices = i * chunks_size end_indices = i * chunks_size + cur_chunk_size - end_indices = tf.Print(end_indices, [chunks_size, cur_chunk_size, start_indices, end_indices], message = "Chunking:") - neg_mask_start = tf.cast(tf.sequence_mask(start_indices, maxlen = max_length - window_size + 1), tf.float32) + neg_mask_start = 1 - tf.cast(tf.sequence_mask(start_indices, maxlen = max_length - window_size + 1), tf.float32) mask_end = tf.cast(tf.sequence_mask(end_indices, maxlen = max_length - window_size + 1), tf.float32) final_mask = tf.multiply(neg_mask_start, mask_end) final_mask = tf.expand_dims(final_mask, axis = 2) final_mask = tf.expand_dims(final_mask, axis = 3) extracted_chunk = tf.multiply(final_mask, detector) - #tf.slice(detector, [0, i * chunks_size, 0, 0], [-1, cur_chunk_size, 1, num_filters]) pooling = tf.nn.max_pool(extracted_chunk, ksize = [1, max_length - window_size + 1, 1, 1], strides = stride, @@ -243,7 +240,6 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ num_filters_total = num_filters * len(window_sizes) * dynamic_max_pooling_p hidden_layer = tf.reshape(concatenated_pools, [-1, num_filters_total]) - hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, initializer_operations From baaedc77fa95f23038c4ec0ee4e674e80a72b642 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 26 Oct 2017 12:55:03 +0200 Subject: [PATCH 089/116] Added attention and sum as output aggregation --- .../lucid_ml/classifying/tensorflow_models.py | 24 ++++++++++++++++--- Code/lucid_ml/run.py | 2 +- 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index d4c4c08..734abe4 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -152,13 +152,30 @@ def create_multilayer_lstm(): h1, h2 = bidi_output_states output_state = tf.concat([h1, h2], axis = 2, name = "concat_bidi_output_states") - if aggregate_output == "average": + if aggregate_output == "sum": + output_state = tf.reduce_sum(output_state, axis = 1) + elif aggregate_output == "average": # average over outputs at all time steps output_state = tf.reduce_sum(output_state, axis = 1) - output_state = tf.div(output_state, tf.cast(tf.reshape(seq_length, [-1, 1]), tf.float32)) + seq_length_reshaped = tf.cast(tf.reshape(seq_length, [-1, 1]), tf.float32) + minimum_length = tf.ones_like(seq_length_reshaped, dtype=tf.float32) + output_state = tf.div(output_state, tf.maximum(seq_length_reshaped, minimum_length)) elif aggregate_output == "last": # return output at last time step output_state = extract_axis_1(output_state, seq_length - 1) + elif aggregate_output == "attention": + # we perform attention according to Hierarchical Attention Network (word attention) + ## compute hidden representation of outputs + ### context vector size as in HN-ATT + context_vector_size = 100 + hidden_output_representation = tf.contrib.layers.fully_connected(output_state, context_vector_size, activation_fn = tf.nn.tanh) + ## compute dot product with context vector + context_vector = tf.Variable(tf.random_normal([context_vector_size], stddev=0.1)) + dot_product = tf.tensordot(hidden_output_representation, context_vector, [[2], [0]]) + ## compute weighted sum + attention_weights = tf.nn.softmax(dot_product) + attention_weights = tf.expand_dims(attention_weights, -1) + output_state = tf.reduce_sum(tf.multiply(output_state, attention_weights), axis = 1) else: raise ValueError("Aggregation method not implemented!") @@ -213,7 +230,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ for i in range(dynamic_max_pooling_p): # make sure we don't get out of bounds at end of sequence - cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 or dynamic_max_pooling_p == 1 else tf.mod(seq_length, dynamic_max_pooling_p) + cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 or dynamic_max_pooling_p == 1 else seq_length - i * chunks_size # create a mask for the entire sequence where only those are selected which are in the current chunk start_indices = i * chunks_size @@ -729,3 +746,4 @@ def predict(self, X): session.close() return result + diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 00bb8f4..4cdf089 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -842,7 +842,7 @@ def _generate_parsers(): the other reads it from right to left. [False]") detailed_options.add_argument('--aggregate_output', type=str, dest='aggregate_output', default="average", help= "How to aggregate the outputs of an LSTM. 'last' uses the output at the last time step. 'average' takes the mean over all outputs. [average]", - choices = ["average", "last"]) + choices = ["average", "last", "attention", "sum"]) neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= From 79c8659a7947d8302462280c5d64888347e80700 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 26 Oct 2017 13:29:50 +0200 Subject: [PATCH 090/116] Move sequence length computation to utils --- Code/lucid_ml/classifying/tensorflow_models.py | 8 +------- Code/lucid_ml/utils/tf_utils.py | 12 ++++++++++++ 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index d4c4c08..86d733a 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -9,7 +9,7 @@ from tensorflow.python.ops import math_ops, random_ops, array_ops from tensorflow.python.layers import utils from datetime import datetime -from utils.tf_utils import tf_normalize +from utils.tf_utils import tf_normalize, sequence_length #tf.logging.set_verbosity(tf.logging.INFO) def _load_embeddings(filename, vocab_size, embedding_size): @@ -72,12 +72,6 @@ def _init_embedding_layer(pretrained_embeddings_path, feature_input, embedding_s return embedded_words, embedding_size -def sequence_length(sequence): - used = tf.sign(sequence) - length = tf.reduce_sum(used, 1) - length = tf.cast(length, tf.int32) - return length - def extract_axis_1(data, ind): """ Get specified elements along the first axis of tensor. diff --git a/Code/lucid_ml/utils/tf_utils.py b/Code/lucid_ml/utils/tf_utils.py index 3fe4657..43a4be8 100644 --- a/Code/lucid_ml/utils/tf_utils.py +++ b/Code/lucid_ml/utils/tf_utils.py @@ -45,6 +45,18 @@ def tf_normalize(X, input_tensor): return scaled_input +def sequence_length(sequence): + """ + Takes as input a tensor of dimensions [batch_size, max_len] which encodes some sequence of maximum length max_len as + a sequence of positive ids. For positions beyond the length of the actual sequence, the id is assumed to be zero (i.e., zero is used for padding). + + This function returns a one-dimensional tensor of size [batch_size], where each entry denotes the length of the corresponding sequence. + """ + used = tf.sign(sequence) + length = tf.reduce_sum(used, 1) + length = tf.cast(length, tf.int32) + return length + if __name__ == "__main__": import doctest doctest.testmod() \ No newline at end of file From fd67da96fb985ae0f277926bb93b4a0dcb1e7560 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 26 Oct 2017 13:30:20 +0200 Subject: [PATCH 091/116] Unit test for sequence length --- Code/lucid_ml/tftests/__init__.py | 0 Code/lucid_ml/tftests/testutils.py | 18 ++++++++++++++++++ 2 files changed, 18 insertions(+) create mode 100644 Code/lucid_ml/tftests/__init__.py create mode 100644 Code/lucid_ml/tftests/testutils.py diff --git a/Code/lucid_ml/tftests/__init__.py b/Code/lucid_ml/tftests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Code/lucid_ml/tftests/testutils.py b/Code/lucid_ml/tftests/testutils.py new file mode 100644 index 0000000..4eed065 --- /dev/null +++ b/Code/lucid_ml/tftests/testutils.py @@ -0,0 +1,18 @@ +from utils.tf_utils import * +import tensorflow as tf + +class SequenceTests(tf.test.TestCase): + + def testSequenceLength(self): + with self.test_session(): + sequences = [[3, 4, 1, 3, 6, 0, 0, 0], + [3, 4, 1, 3, 6, 4, 0, 0], + [3, 4, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0], + [3, 0, 0, 0, 0, 0, 0, 0], + [3, 4, 1, 3, 6, 1, 1, 1]] + lengths = sequence_length(sequences) + self.assertAllEqual(lengths.eval(), [5,6,3,0,1,8]) + +if __name__ == '__main__': + tf.test.main() \ No newline at end of file From 9cc274faa4495c2ffb5e7885026f8809c8d48a0c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 26 Oct 2017 14:49:03 +0200 Subject: [PATCH 092/116] Test averaging LSTM outputs --- .../lucid_ml/classifying/tensorflow_models.py | 8 ++----- Code/lucid_ml/tftests/testutils.py | 23 +++++++++++++++++++ Code/lucid_ml/utils/tf_utils.py | 21 +++++++++++++++++ 3 files changed, 46 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index bad75c3..e2084ea 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -9,7 +9,7 @@ from tensorflow.python.ops import math_ops, random_ops, array_ops from tensorflow.python.layers import utils from datetime import datetime -from utils.tf_utils import tf_normalize, sequence_length +from utils.tf_utils import tf_normalize, sequence_length, average_outputs #tf.logging.set_verbosity(tf.logging.INFO) def _load_embeddings(filename, vocab_size, embedding_size): @@ -149,11 +149,7 @@ def create_multilayer_lstm(): if aggregate_output == "sum": output_state = tf.reduce_sum(output_state, axis = 1) elif aggregate_output == "average": - # average over outputs at all time steps - output_state = tf.reduce_sum(output_state, axis = 1) - seq_length_reshaped = tf.cast(tf.reshape(seq_length, [-1, 1]), tf.float32) - minimum_length = tf.ones_like(seq_length_reshaped, dtype=tf.float32) - output_state = tf.div(output_state, tf.maximum(seq_length_reshaped, minimum_length)) + output_state = average_outputs(output_state, seq_length) elif aggregate_output == "last": # return output at last time step output_state = extract_axis_1(output_state, seq_length - 1) diff --git a/Code/lucid_ml/tftests/testutils.py b/Code/lucid_ml/tftests/testutils.py index 4eed065..33c87ab 100644 --- a/Code/lucid_ml/tftests/testutils.py +++ b/Code/lucid_ml/tftests/testutils.py @@ -13,6 +13,29 @@ def testSequenceLength(self): [3, 4, 1, 3, 6, 1, 1, 1]] lengths = sequence_length(sequences) self.assertAllEqual(lengths.eval(), [5,6,3,0,1,8]) + + +class AggregationTests(tf.test.TestCase): + + def testAverage(self): + with self.test_session(): + # output dimensions = [3, 4, 2] + sequences = [[3, 4, 1, 3], + [0, 0, 0, 0], + [3, 0, 0, 0], + [1, 2, 0, 0]] + outputs = [[[1., 1.], [1., 1.], [2., 2.5], [3., 4.]], + [[1., 2.], [3., 4.], [0., 0.], [0., 0.]], + [[5., 6.], [10., 10.,], [10., 10.], [10., 10.]], + [[10., 12.], [0., 0.,],[0., 0.,],[0., 0.,]]] + outputs = tf.constant(outputs, dtype = tf.float32) + expected_result = [[1.75, 2.125], + [0., 0.], + [5., 6.], + [5., 6.]] + lengths = sequence_length(sequences) + result = average_outputs(outputs, lengths) + self.assertAllEqual(result.eval(), expected_result) if __name__ == '__main__': tf.test.main() \ No newline at end of file diff --git a/Code/lucid_ml/utils/tf_utils.py b/Code/lucid_ml/utils/tf_utils.py index 43a4be8..39c76ce 100644 --- a/Code/lucid_ml/utils/tf_utils.py +++ b/Code/lucid_ml/utils/tf_utils.py @@ -57,6 +57,27 @@ def sequence_length(sequence): length = tf.cast(length, tf.int32) return length +def average_outputs(outputs, seq_length): + """ + Given the padded outputs of an RNN and the actual length of the sequence, this function computes the average + over all (non-padded) outputs. In the special case where the length is 0, the function returns 0. + + Parameters + ---------- + outputs : tensor of shape [batch_size, max_length, output_dimensions] + The output from an RNN with hidden representation size 'output_dimensions'. + seq_length : tensor of shape [batch_size] containing the number of valid outputs in 'outputs'. + """ + # average over outputs at all time steps + seq_mask = tf.cast(tf.sequence_mask(seq_length, maxlen = outputs.get_shape().as_list()[1]), tf.float32) + seq_mask = tf.expand_dims(seq_mask, axis = 2) + outputs = outputs * seq_mask + output_state = tf.reduce_sum(outputs, axis = 1) + seq_length_reshaped = tf.cast(tf.reshape(seq_length, [-1, 1]), tf.float32) + minimum_length = tf.ones_like(seq_length_reshaped, dtype=tf.float32) + output_state = tf.div(output_state, tf.maximum(seq_length_reshaped, minimum_length)) + return output_state + if __name__ == "__main__": import doctest doctest.testmod() \ No newline at end of file From 89408d4d119afe7403752a219826b265a16806de Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 26 Oct 2017 15:42:32 +0200 Subject: [PATCH 093/116] No input normalization for SNNs, implementation of swish --- Code/lucid_ml/classifying/tensorflow_models.py | 11 ++++++++++- Code/lucid_ml/run.py | 2 +- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index e2084ea..cfd97da 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -146,6 +146,7 @@ def create_multilayer_lstm(): h1, h2 = bidi_output_states output_state = tf.concat([h1, h2], axis = 2, name = "concat_bidi_output_states") + # note that dynamic_rnn returns zero outputs after seq_length if aggregate_output == "sum": output_state = tf.reduce_sum(output_state, axis = 1) elif aggregate_output == "average": @@ -326,7 +327,7 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer params_predict = {dropout_tensor : 1} # we need to have the input data scaled such they have mean 0 and variance 1 - if self_normalizing or standard_normal: + if standard_normal: scaled_input = tf_normalize(X, x_tensor) else: scaled_input = x_tensor @@ -355,6 +356,12 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer return x_tensor, y_tensor, hidden_layer, params_fit, params_predict, [] +def swish(x): + """ + Implementation of Swish: a Self-Gated Activation Function (https://arxiv.org/abs/1710.05941) + """ + return x * tf.sigmoid(x) + def _transform_activation_function(func): if func == "relu": hidden_activation_function = tf.nn.relu @@ -362,6 +369,8 @@ def _transform_activation_function(func): hidden_activation_function = tf.nn.tanh elif func == "identity": hidden_activation_function = tf.identity + elif func == "swish": + hidden_activation_function = swish return hidden_activation_function def mlp_base(hidden_activation_function = "relu"): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 4cdf089..a7a63f7 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -820,7 +820,7 @@ def _generate_parsers(): followed by the entries of its vectors, separated by blanks. If None is specified, the word embeddings are zero-initialized and trained\ jointly with the classification task. [None]") neural_network_options.add_argument('--hidden_activation_function', type=str, dest="hidden_activation_function", default="relu", help= - "Specify the activation function used on the hidden layers in MLP-Base and MLP-Soph. [relu]", choices = ["relu", "tanh", "identity"]) + "Specify the activation function used on the hidden layers in MLP-Base and MLP-Soph. [relu]", choices = ["relu", "tanh", "identity", "swish"]) neural_network_options.add_argument('--trainable_embeddings', action="store_true", dest="trainable_embeddings", default=False, help= "Whether to keep training the pretrained embeddings further with classification the task or not. [False]") neural_network_options.add_argument('--hidden_layers', type=int, dest="hidden_layers", nargs='+', default=[1000], help= From 94b853903a3eecae6f5adff1ab94f88a7204c72b Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 27 Oct 2017 11:27:24 +0200 Subject: [PATCH 094/116] Moved dynamic max pooling to tf-utils and tested it. --- .../lucid_ml/classifying/tensorflow_models.py | 30 +------- Code/lucid_ml/tftests/testutils.py | 49 +++++++++++++ Code/lucid_ml/utils/tf_utils.py | 68 +++++++++++++++++++ 3 files changed, 119 insertions(+), 28 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index cfd97da..a53ab66 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -9,7 +9,7 @@ from tensorflow.python.ops import math_ops, random_ops, array_ops from tensorflow.python.layers import utils from datetime import datetime -from utils.tf_utils import tf_normalize, sequence_length, average_outputs +from utils.tf_utils import tf_normalize, sequence_length, average_outputs, dynamic_max_pooling #tf.logging.set_verbosity(tf.logging.INFO) def _load_embeddings(filename, vocab_size, embedding_size): @@ -215,33 +215,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ bias = tf.Variable(tf.random_normal([num_filters])) detector = tf.nn.relu(tf.nn.bias_add(conv, bias)) - # dynamic max-pooling: extract maximum for each chunk - chunks_size = tf.ceil(tf.divide(seq_length, dynamic_max_pooling_p)) - chunk_poolings = [] - for i in range(dynamic_max_pooling_p): - - # make sure we don't get out of bounds at end of sequence - cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 or dynamic_max_pooling_p == 1 else seq_length - i * chunks_size - - # create a mask for the entire sequence where only those are selected which are in the current chunk - start_indices = i * chunks_size - end_indices = i * chunks_size + cur_chunk_size - - neg_mask_start = 1 - tf.cast(tf.sequence_mask(start_indices, maxlen = max_length - window_size + 1), tf.float32) - mask_end = tf.cast(tf.sequence_mask(end_indices, maxlen = max_length - window_size + 1), tf.float32) - final_mask = tf.multiply(neg_mask_start, mask_end) - final_mask = tf.expand_dims(final_mask, axis = 2) - final_mask = tf.expand_dims(final_mask, axis = 3) - - extracted_chunk = tf.multiply(final_mask, detector) - pooling = tf.nn.max_pool(extracted_chunk, - ksize = [1, max_length - window_size + 1, 1, 1], - strides = stride, - padding = "VALID") - pooling = tf.reshape(pooling, [-1, num_filters]) - chunk_poolings.append(pooling) - - concatenated_pooled_chunks = tf.concat(chunk_poolings, 1) + concatenated_pooled_chunks = dynamic_max_pooling(detector, seq_length, max_length, num_filters, window_size, dynamic_max_pooling_p = dynamic_max_pooling_p) pooled_outputs.append(concatenated_pooled_chunks) concatenated_pools = tf.concat(pooled_outputs, 1) diff --git a/Code/lucid_ml/tftests/testutils.py b/Code/lucid_ml/tftests/testutils.py index 33c87ab..a0e3e21 100644 --- a/Code/lucid_ml/tftests/testutils.py +++ b/Code/lucid_ml/tftests/testutils.py @@ -36,6 +36,55 @@ def testAverage(self): lengths = sequence_length(sequences) result = average_outputs(outputs, lengths) self.assertAllEqual(result.eval(), expected_result) + + +class DynamicMaxPoolingTests(tf.test.TestCase): + + def testPoolingMask(self): + pass + + def testDynamicMaxPooling(self): + with self.test_session(): + + # add dimension to detector stage + window_size = 1 + num_filters = 3 + max_length = 3 + map_size = max_length - window_size + 1 + #detector : tensor of shape [batch_size, map_size, 1, num_filters] + detector = [[[1., 1., 3.], [2., 2.5, 1.5], [3., 4., 2.]], + [[1., 2., 0.5], [0., 0., 1.], [0., 0., 1.]], + [[5., 6., 20.], [10., 10., 9.5], [10., 10., 11.]], + [[10., 12., 0.], [4., 3., 1.],[2., 2., 7.]]] + detector = tf.expand_dims(detector, axis = 2) + + seq_length = tf.constant([0, 1, 2, 3], dtype = tf.float32) + + # p = 1 + result = dynamic_max_pooling(detector, seq_length, max_length, num_filters, window_size, dynamic_max_pooling_p = 1) + expected_result = [[0., 0., 0.], + [1., 2., 0.5], + [10., 10., 20.], + [10., 12., 7.]] + self.assertAllEqual(result.eval(), expected_result) + + # full sequence length, p = 2, so we check if we can handle the case where text-length divided by number of chunks is not an integer + seq_length = tf.constant([3, 3, 3, 3], dtype = tf.float32) + result = dynamic_max_pooling(detector, seq_length, max_length, num_filters, window_size, dynamic_max_pooling_p = 2) + expected_result = [[2., 2.5, 3., 3., 4., 2.], + [1., 2., 1., 0., 0., 1.], + [10., 10., 20., 10., 10., 11.], + [10., 12., 1., 2., 2., 7. ]] + self.assertAllEqual(result.eval(), expected_result) + + # full sequence length, p = 3, which should collapse the sequence dimension of the detector outputs in this case + seq_length = tf.constant([3, 3, 3, 3], dtype = tf.float32) + result = dynamic_max_pooling(detector, seq_length, max_length, num_filters, window_size, dynamic_max_pooling_p = 3) + expected_result = [[1., 1., 3., 2., 2.5, 1.5, 3., 4., 2.], + [1., 2., 0.5, 0., 0., 1., 0., 0., 1.], + [5., 6., 20., 10., 10., 9.5, 10., 10., 11.], + [10., 12., 0., 4., 3., 1., 2., 2., 7.]] + self.assertAllEqual(result.eval(), expected_result) if __name__ == '__main__': tf.test.main() \ No newline at end of file diff --git a/Code/lucid_ml/utils/tf_utils.py b/Code/lucid_ml/utils/tf_utils.py index 39c76ce..d07197a 100644 --- a/Code/lucid_ml/utils/tf_utils.py +++ b/Code/lucid_ml/utils/tf_utils.py @@ -57,6 +57,69 @@ def sequence_length(sequence): length = tf.cast(length, tf.int32) return length +def dynamic_max_pooling(detector, seq_length, max_length, num_filters, window_size, dynamic_max_pooling_p = 1): + """ + Performs dynamic max pooling as in [XML-CNN]. That is, it splits the outputs of the detector stage into 'p' chunks, performs + max-pooling on each chunk and concatenates the outputs. The function assumes the CNN from which the detector stage results to use 'VALID' + padding as well as strides = [1, 1, 1, 1]. + + If the length of the sequence can not be divided into evenly sized chunks, we make the last chunk contain the remainder of the text. + + References + ---------- + [XML-CNN] J Liu, WC Chang, Y Wu, Y Yang + "Deep Learning for Extreme Multi-label Text Classification" + Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval + + Parameters + ---------- + detector : tensor of shape [batch_size, map_size, 1, num_filters] + The output from the 'detector' stage of a CNN with a configuration (number of filters, window_size, ...) such that it results in + a tensor with the according feature map size and number of filters. + seq_length : tensor of shape [batch_size] containing the length of the text. + max_length : maximum length of the sequence + num_filters : number of filters used in the CNN + window_size : window size used in the CNN + dynamic_max_pooling_p : the number of chunks 'p' to split the outputs of the detector stage into. + + Returns + ------- + Tensor of shape [batch_size, p * num_filters] + The concatenated outputs of the max-pooling operations over the 'p' chunks. + """ + # assumptions of CNN from which the detector outputs result + stride = [1,1,1,1] + detector_output_length = seq_length - window_size + 1 + + # dynamic max-pooling: extract maximum for each chunk + chunks_size = tf.ceil(tf.divide(detector_output_length, dynamic_max_pooling_p)) + chunk_poolings = [] + for i in range(dynamic_max_pooling_p): + + # make sure we don't get out of bounds at end of sequence + cur_chunk_size = chunks_size if i != dynamic_max_pooling_p - 1 or dynamic_max_pooling_p == 1 else detector_output_length - i * chunks_size + + # create a mask for the entire sequence where only those are selected which are in the current chunk + start_indices = i * chunks_size + end_indices = i * chunks_size + cur_chunk_size + + neg_mask_start = 1 - tf.cast(tf.sequence_mask(start_indices, maxlen = max_length - window_size + 1), tf.float32) + mask_end = tf.cast(tf.sequence_mask(end_indices, maxlen = max_length - window_size + 1), tf.float32) + final_mask = tf.multiply(neg_mask_start, mask_end) + final_mask = tf.expand_dims(final_mask, axis = 2) + final_mask = tf.expand_dims(final_mask, axis = 3) + + extracted_chunk = tf.multiply(final_mask, detector) + pooling = tf.nn.max_pool(extracted_chunk, + ksize = [1, max_length - window_size + 1, 1, 1], + strides = stride, + padding = "VALID") + pooling = tf.reshape(pooling, [-1, num_filters]) + chunk_poolings.append(pooling) + + concatenated_pooled_chunks = tf.concat(chunk_poolings, 1) + return concatenated_pooled_chunks + def average_outputs(outputs, seq_length): """ Given the padded outputs of an RNN and the actual length of the sequence, this function computes the average @@ -67,6 +130,11 @@ def average_outputs(outputs, seq_length): outputs : tensor of shape [batch_size, max_length, output_dimensions] The output from an RNN with hidden representation size 'output_dimensions'. seq_length : tensor of shape [batch_size] containing the number of valid outputs in 'outputs'. + + Returns + ------- + Tensor of shape [batch_size, output_dimensions] + The average over all outputs in the sequence. """ # average over outputs at all time steps seq_mask = tf.cast(tf.sequence_mask(seq_length, maxlen = outputs.get_shape().as_list()[1]), tf.float32) From aa2088ad7fa58735b756d8e58d0ccb76b5a84be9 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 27 Oct 2017 15:06:50 +0200 Subject: [PATCH 095/116] Option for iterating LSTM until max-length --- Code/lucid_ml/classifying/tensorflow_models.py | 16 ++++++++++++---- Code/lucid_ml/run.py | 6 +++++- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index cfd97da..ecc8e5f 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -91,7 +91,8 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = pretrained_embeddings_path = None, trainable_embeddings = True, variational_recurrent_dropout = True, - bidirectional = False): + bidirectional = False, + iterate_until_maxlength = False): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -102,7 +103,13 @@ def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = params_fit = {dropout_tensor : keep_prob_dropout} params_predict = {dropout_tensor : 1} - seq_length = sequence_length(feature_input) + if iterate_until_maxlength: + # create a vector of correct shape and set to maxlen + seq_length = tf.reduce_sum(feature_input, 1) + seq_length = tf.cast(seq_length, tf.int32) + seq_length = feature_input.get_shape().as_list()[1] * tf.ones_like(seq_length) + else: + seq_length = sequence_length(feature_input) initializer_operations = [] @@ -397,14 +404,15 @@ def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_ dynamic_max_pooling_p = dynamic_max_pooling_p) def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout, - bidirectional, aggregate_output): + bidirectional, aggregate_output, iterate_until_maxlength): return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, trainable_embeddings = trainable_embeddings, variational_recurrent_dropout=variational_recurrent_dropout, bidirectional = bidirectional, - aggregate_output = aggregate_output) + aggregate_output = aggregate_output, + iterate_until_maxlength = iterate_until_maxlength) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index a7a63f7..483d981 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -624,7 +624,8 @@ def create_classifier(options, num_concepts): trainable_embeddings = options.trainable_embeddings, variational_recurrent_dropout = options.variational_recurrent_dropout, bidirectional = options.bidirectional, - aggregate_output = options.aggregate_output), + aggregate_output = options.aggregate_output, + iterate_until_maxlength = options.iterate_until_maxlength), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, gpu_memory_fraction = options.memory), @@ -840,6 +841,9 @@ def _generate_parsers(): neural_network_options.add_argument('--bidirectional', action="store_true", dest="bidirectional", default=False, help= "When activated, we create two instances of (potentially multi-layered) LSTMs, where one reads the input from left to right and \ the other reads it from right to left. [False]") + neural_network_options.add_argument('--iterate_until_maxlength', action="store_true", dest="iterate_until_maxlength", default=False, help= + "When activated, the LSTM always iterates max_features steps, even if the actual sequence is shorter. Instead, it consumes\ + at each additional step the padding symbol. The outputs of steps beyond the actual sequence length are taken into account as well for output aggregation. [False]") detailed_options.add_argument('--aggregate_output', type=str, dest='aggregate_output', default="average", help= "How to aggregate the outputs of an LSTM. 'last' uses the output at the last time step. 'average' takes the mean over all outputs. [average]", choices = ["average", "last", "attention", "sum"]) From 4a91c40945e8519304abab17929cdae0d24edf89 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Mon, 30 Oct 2017 19:59:30 +0100 Subject: [PATCH 096/116] First iteration of Meta-Labeler --- .../lucid_ml/classifying/tensorflow_models.py | 134 ++++++++++++++++-- Code/lucid_ml/run.py | 28 +++- 2 files changed, 147 insertions(+), 15 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 03df836..1ad068a 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -4,6 +4,7 @@ import scipy.sparse as sps from sklearn.base import BaseEstimator from sklearn.metrics import f1_score +from sklearn.preprocessing import LabelBinarizer import math, numbers, os from tensorflow.python.framework import ops, tensor_shape, tensor_util from tensorflow.python.ops import math_ops, random_ops, array_ops @@ -423,6 +424,7 @@ def _batch_generator(self): return X_batch, y_batch else: return X_batch + class MultiLabelSKFlow(BaseEstimator): """ @@ -446,7 +448,11 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre hidden_activation_function = tf.nn.relu, bottleneck_layers = None, hidden_keep_prob = 0.5, - gpu_memory_fraction = 1.): + gpu_memory_fraction = 1., + meta_labeler_phi = None, + meta_labeler_alpha = 0.1, + meta_labeler_min_labels = 1, + meta_labeler_max_labels = None): """ """ @@ -462,6 +468,13 @@ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), thre self.bottleneck_layers = bottleneck_layers self.hidden_keep_prob = hidden_keep_prob + # configuration for meta-labeler + self.meta_labeler_phi = meta_labeler_phi + self.meta_labeler_alpha = meta_labeler_alpha + self.num_label_binarizer = None + self.meta_labeler_max_labels = meta_labeler_max_labels + self.meta_labeler_min_labels = meta_labeler_min_labels + # used by this class self.validation_metric = validation_metric self.optimize_threshold = optimize_threshold @@ -491,21 +504,90 @@ def _get_save_model_path(self): def _calc_num_steps(self, X): return int(np.ceil(X.shape[0] / self.batch_size)) + + def _predict_batch(self, X_batch): + feed_dict = {self.x_tensor: X_batch} + feed_dict.update(self.params_predict) + + if self.meta_labeler_phi is None: + predictions = self.session.run(self.predictions, feed_dict = feed_dict) + else: + predictions = self.session.run([self.predictions, self.meta_labeler_prediction], feed_dict = feed_dict) + + return predictions + + def _make_binary_decision(self, predictions): + if self.meta_labeler_phi is None: + y_pred = predictions > self.threshold + else: + #TODO: implement meta-labeler label assignment + predictions, meta_labeler_predictions = predictions + max_probability = np.argmax(meta_labeler_predictions, axis = 1) + meta_labeler_predictions = np.zeros_like(meta_labeler_predictions) + meta_labeler_predictions[max_probability] = 1 + meta_labeler_predictions = self.num_label_binarizer.inverse_transform(meta_labeler_predictions, 0) + y_pred = np.zeros_like(predictions) + for i in range(predictions.shape[0]): + num_labels_for_sample = meta_labeler_predictions[i] + top_indices = (-predictions[i,:]).argsort()[:num_labels_for_sample] + y_pred[i,top_indices] = 1 + + return csr_matrix(y_pred) + def _compute_validation_score(self, session, X_val_batch, y_val_batch): - feed_dict = {self.x_tensor: X_val_batch, self.y_tensor: y_val_batch} + feed_dict = {self.x_tensor: X_val_batch} feed_dict.update(self.params_predict) if self.validation_metric == "val_loss": return session.run(self.loss, feed_dict = feed_dict) elif callable(self.validation_metric): - predictions = session.run(self.predictions, feed_dict = feed_dict) - y_pred = csr_matrix(predictions > self.threshold) + predictions = self._predict_batch(X_val_batch) + y_pred = self._make_binary_decision(predictions) if self.optimize_threshold: return self.validation_metric(y_val_batch, y_pred), predictions else: return self.validation_metric(y_val_batch, y_pred) + + def _num_labels_discrete(self, y, min_number_labels = 1, max_number_labels = None): + """ + Counts for each row in 'y' how many of the columns are set to 1. Outputs the result in turn as a binary indicator matrix where + the columns 0, ..., m correspond to 'min_number_labels', 'min_number_labels' + 1, ..., 'max_number_labels'. + + Parameters + ---------- + y: (sparse) numpy array of shape [n_samples, n_classes] + An indicator matrix denoting which classes are assigned to a sample (multiple columns per row may be 1) + min_number_labels: int, default=1 + Minimum number of labels each sample has to have. If a sample has less than 'min_number_labels' assigned, + the corresponding output is set to 'min_number_labels'. + max_number_labels: int, default=None + Maximum number of labels each sample has to have. If a sample has more than 'min_number_labels' assigned, + the corresponding output is set to 'max_number_labels'. If 'max_number_labels' is None, it is set to the max number found + in y. + Returns + --------- + num_samples_y: (sparse) numpy array of shape [n_samples, max_number_samples - min_number_samples + 1] + """ + + num_samples_y = np.array(np.sum(y, axis = 1)) + num_samples_y = num_samples_y.reshape(-1) + num_samples_y[num_samples_y < min_number_labels] = min_number_labels + + if max_number_labels is None: + max_number_labels = np.max(num_samples_y) + + num_samples_y[num_samples_y > max_number_labels] = max_number_labels + + # 'fit' method calls this + if self.num_label_binarizer is None: + self.num_label_binarizer = LabelBinarizer() + self.num_label_binarizer.fit(num_samples_y) + + indicator_matrix_num_labels = self.num_label_binarizer.transform(num_samples_y) + return indicator_matrix_num_labels + def fit(self, X, y): self.y = y @@ -552,18 +634,44 @@ def fit(self, X, y): #logits = tf.identity(logits, name='logits') logits = tf.contrib.layers.linear(self.last_layer, num_outputs=y.shape[1]) + # Loss and Optimizer losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y_tensor) loss = tf.reduce_sum(losses, axis = 1) self.loss = tf.reduce_mean(loss, axis = 0) - optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) # prediction self.predictions = tf.sigmoid(logits) + if self.meta_labeler_phi is not None: + + # compute target of meta labeler + y_num_labels = self._num_labels_discrete(y_train, min_number_labels=self.meta_labeler_min_labels,max_number_labels=self.meta_labeler_max_labels) + y_num_labels_tensor = tf.placeholder(tf.float32, shape=(None, y_num_labels.shape[1]), name = "y_num_labels") + + # compute logits of meta labeler + if self.meta_labeler_phi == "content": + meta_logits = tf.contrib.layers.linear(self.last_layer, num_outputs=y_num_labels.shape[1]) + elif self.meta_labeler_phi == "score": + meta_logits = tf.contrib.layers.linear(tf.nn.softmax(logits), num_outputs=y_num_labels.shape[1]) + + # compute loss of meta labeler + meta_labeler_loss = tf.nn.softmax_cross_entropy_with_logits(labels = y_num_labels_tensor, logits = meta_logits) + self.meta_labeler_loss = tf.reduce_mean(meta_labeler_loss, axis = 0) + + # compute prediction of meta labeler + self.meta_labeler_prediction = tf.nn.softmax(meta_logits) + + # add meta labeler loss to labeling loss + self.loss = (1 - self.meta_labeler_alpha) * self.loss + self.meta_labeler_alpha * self.meta_labeler_loss + + # optimize + optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) + gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.gpu_memory_fraction) session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) + self.session = session # Initializing the variables session.run(tf.global_variables_initializer()) for (init_op, init_op_feed_dict) in initializer_operations: @@ -589,6 +697,10 @@ def fit(self, X, y): X_batch, y_batch = batch_generator._batch_generator() feed_dict = {self.x_tensor: X_batch, self.y_tensor: y_batch} feed_dict.update(self.params_fit) + + if self.meta_labeler_phi is not None: + feed_dict.update({y_num_labels_tensor : self._num_labels_discrete(y_batch)}) + session.run(optimizer, feed_dict = feed_dict) # overwrite parameter values for prediction step @@ -657,8 +769,7 @@ def fit(self, X, y): format(epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, loss, avg_validation_score, best_validation_score, self.threshold), end='\r') epoch += 1 - - self.session = session + print('') print("Training of TensorFlow model finished!") @@ -716,12 +827,11 @@ def predict(self, X): prediction_steps = self._calc_num_steps(X) for i in range(prediction_steps): X_batch = batch_generator._batch_generator() - feed_dict = {self.x_tensor: X_batch} - feed_dict.update(self.params_predict) - pred_batch = session.run(self.predictions, feed_dict = feed_dict) - prediction[i * self.batch_size:(i+1) * self.batch_size, :] = pred_batch + preds = self._predict_batch(X_batch) + binary_decided_preds = self._make_binary_decision(preds) + prediction[i * self.batch_size:(i+1) * self.batch_size, :] = binary_decided_preds.todense() - result = csr_matrix(prediction > self.threshold) + result = csr_matrix(prediction) # close the session, since no longer needed session.close() diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 483d981..8f78a58 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -595,7 +595,11 @@ def create_classifier(options, num_concepts): num_steps_before_validation = options.num_steps_before_validation, bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, - gpu_memory_fraction = options.memory), + gpu_memory_fraction = options.memory, + meta_labeler_phi = options.meta_labeler_phi, + meta_labeler_alpha = options.meta_labeler_alpha, + meta_labeler_min_labels = options.meta_labeler_min_labels, + meta_labeler_max_labels = options.meta_labeler_max_labels), "cnn": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -610,7 +614,11 @@ def create_classifier(options, num_concepts): dynamic_max_pooling_p=options.dynamic_max_pooling_p), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, - gpu_memory_fraction = options.memory), + gpu_memory_fraction = options.memory, + meta_labeler_phi = options.meta_labeler_phi, + meta_labeler_alpha = options.meta_labeler_alpha, + meta_labeler_min_labels = options.meta_labeler_min_labels, + meta_labeler_max_labels = options.meta_labeler_max_labels), "lstm": MultiLabelSKFlow(batch_size = options.batch_size, num_epochs=options.max_iterations, learning_rate = options.learning_rate, @@ -628,7 +636,11 @@ def create_classifier(options, num_concepts): iterate_until_maxlength = options.iterate_until_maxlength), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, - gpu_memory_fraction = options.memory), + gpu_memory_fraction = options.memory, + meta_labeler_phi = options.meta_labeler_phi, + meta_labeler_alpha = options.meta_labeler_alpha, + meta_labeler_min_labels = options.meta_labeler_min_labels, + meta_labeler_max_labels = options.meta_labeler_max_labels), "nam": ThresholdingPredictor(MLP(verbose=options.verbose, final_activation='sigmoid', batch_size = options.batch_size, learning_rate = options.learning_rate, epochs = options.max_iterations), @@ -851,6 +863,16 @@ def _generate_parsers(): "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= "Specify the number of chunks (p) to perform max-pooling over. [1]") + neural_network_options.add_argument('--meta_labeler_min_labels', type=int, dest="meta_labeler_min_labels", default=1, help= + "Specify the minimum number of labels to assign the meta labeler can predict. [1]") + neural_network_options.add_argument('--meta_labeler_max_labels', type=int, dest="meta_labeler_max_labels", default=None, help= + "Specify the maximum number of labels to assign the meta labeler can predict. When 'None' is specified, the maximum \ + is computed from the data. [None]") + detailed_options.add_argument('--meta_labeler_phi', type=str, dest='meta_labeler_phi', default=None, help= + "Specify whether to predict number of labels from 'score' or from 'content', or whether to use meta labeler at all (None). [None]", + choices = ["content", "score"]) + neural_network_options.add_argument('--meta_labeler_alpha', type=float, dest="meta_labeler_alpha", default=0.1, help= + "The alpha-weight of predicting the correct number of labels when doing meta-labeling. [0.1]") # persistence_options persistence_options = parser.add_argument_group("Feature Persistence Options") From 05bb3639deb3f5c1f427ec59f8f19e73263342d3 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 31 Oct 2017 17:51:26 +0100 Subject: [PATCH 097/116] Print progress when using meta-labeler --- .../lucid_ml/classifying/tensorflow_models.py | 33 ++++++++++++++++--- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 1ad068a..888b405 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -549,6 +549,22 @@ def _compute_validation_score(self, session, X_val_batch, y_val_batch): return self.validation_metric(y_val_batch, y_pred), predictions else: return self.validation_metric(y_val_batch, y_pred) + + def _print_progress(self, epoch, batch_i, steps_per_epoch, avg_validation_score, best_validation_score, total_loss, meta_loss, label_loss): + + progress_string = 'Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Score: {:0.4f}, Best Validation-Score: {:0.4f}' + format_parameters = [epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, + total_loss, avg_validation_score, best_validation_score] + if self.meta_labeler_phi is None: + progress_string += ', Threshold: {:0.2f}' + format_parameters.append(self.threshold) + + else: + progress_string += ', Label-Loss: {:0.4f}, Meta-Loss: {:0.4f}' + format_parameters.extend([label_loss, meta_loss]) + + progress_string = progress_string.format(*format_parameters) + print(progress_string, end='\r') def _num_labels_discrete(self, y, min_number_labels = 1, max_number_labels = None): """ @@ -639,7 +655,7 @@ def fit(self, X, y): # Loss and Optimizer losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y_tensor) loss = tf.reduce_sum(losses, axis = 1) - self.loss = tf.reduce_mean(loss, axis = 0) + self.label_loss = tf.reduce_mean(loss, axis = 0) # prediction self.predictions = tf.sigmoid(logits) @@ -664,7 +680,9 @@ def fit(self, X, y): self.meta_labeler_prediction = tf.nn.softmax(meta_logits) # add meta labeler loss to labeling loss - self.loss = (1 - self.meta_labeler_alpha) * self.loss + self.meta_labeler_alpha * self.meta_labeler_loss + self.loss = (1 - self.meta_labeler_alpha) * self.label_loss + self.meta_labeler_alpha * self.meta_labeler_loss + else: + self.loss = self.label_loss # optimize optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) @@ -705,7 +723,13 @@ def fit(self, X, y): # overwrite parameter values for prediction step feed_dict.update(self.params_predict) - loss = session.run(self.loss, feed_dict = feed_dict) + + # compute losses to track progress + if self.meta_labeler_phi is not None: + total_loss, label_loss, meta_loss = session.run([self.loss, self.label_loss, self.meta_labeler_loss], feed_dict = feed_dict) + else: + total_loss = session.run(self.loss, feed_dict = feed_dict) + label_loss, meta_loss = None, None batches_counter += 1 is_last_epoch = epoch == self.num_epochs - 1 @@ -765,8 +789,7 @@ def fit(self, X, y): break # print progress - print('Epoch {:>2}/{:>2}, Batch {:>2}/{:>2}, Loss: {:0.4f}, Validation-Score: {:0.4f}, Best Validation-Score: {:0.4f}, Threshold: {:0.2f}'. - format(epoch + 1, self.num_epochs, batch_i + 1, steps_per_epoch, loss, avg_validation_score, best_validation_score, self.threshold), end='\r') + self._print_progress(epoch, batch_i, steps_per_epoch, avg_validation_score, best_validation_score, total_loss, meta_loss, label_loss) epoch += 1 From fc0fa6f5262c049bebc8dd79095c2a086429fef5 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 31 Oct 2017 18:12:30 +0100 Subject: [PATCH 098/116] Fix in extracting the correct number of labels to predict. --- Code/lucid_ml/classifying/tensorflow_models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 888b405..b8d65ab 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -522,9 +522,10 @@ def _make_binary_decision(self, predictions): else: #TODO: implement meta-labeler label assignment predictions, meta_labeler_predictions = predictions - max_probability = np.argmax(meta_labeler_predictions, axis = 1) + max_probability_cols = np.argmax(meta_labeler_predictions, axis = 1) + max_probability_indices = tuple(np.indices([meta_labeler_predictions.shape[0]]))+(max_probability_cols,) meta_labeler_predictions = np.zeros_like(meta_labeler_predictions) - meta_labeler_predictions[max_probability] = 1 + meta_labeler_predictions[max_probability_indices] = 1 meta_labeler_predictions = self.num_label_binarizer.inverse_transform(meta_labeler_predictions, 0) y_pred = np.zeros_like(predictions) for i in range(predictions.shape[0]): From 37d8f037a48ffadaf773cd6d6bc457431ffe6ae6 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 31 Oct 2017 20:34:30 +0100 Subject: [PATCH 099/116] Compute score-based Meta-Labeler based on label predictions. --- Code/lucid_ml/classifying/tensorflow_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index b8d65ab..a8d9cc6 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -671,7 +671,7 @@ def fit(self, X, y): if self.meta_labeler_phi == "content": meta_logits = tf.contrib.layers.linear(self.last_layer, num_outputs=y_num_labels.shape[1]) elif self.meta_labeler_phi == "score": - meta_logits = tf.contrib.layers.linear(tf.nn.softmax(logits), num_outputs=y_num_labels.shape[1]) + meta_logits = tf.contrib.layers.linear(self.predictions, num_outputs=y_num_labels.shape[1]) # compute loss of meta labeler meta_labeler_loss = tf.nn.softmax_cross_entropy_with_logits(labels = y_num_labels_tensor, logits = meta_logits) From 592dc091650c68cd78890584d507e56aed01e882 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 31 Oct 2017 22:23:40 +0100 Subject: [PATCH 100/116] Grid search file now can have string values --- Code/lucid_ml/run.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 8f78a58..f4f4061 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -426,6 +426,8 @@ def _make_space(options): cast_func = int elif param_type == "float": cast_func = float + elif param_type == "string": + cast_func = str # all possible values space[param_name] = list(map(cast_func, info[2:])) From c0d63c01569909679d9775f64eb60be38aa10b54 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 7 Nov 2017 15:09:20 +0100 Subject: [PATCH 101/116] Option for using batch normalization in MLP. --- Code/lucid_ml/classifying/tensorflow_models.py | 15 +++++++++------ Code/lucid_ml/run.py | 3 +++ 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index a8d9cc6..bd465d1 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -297,7 +297,7 @@ def selu(x): return scale*tf.where(x>=0.0, x, alpha*tf.nn.elu(x)) def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], self_normalizing = False, hidden_activation_function = tf.nn.relu, - standard_normal = False): + standard_normal = False, batch_norm = True): """Model function for MLP-Soph.""" # convert sparse tensors to dense @@ -326,7 +326,8 @@ def mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layer hidden_layer = embedding_layer for hidden_units in hidden_layers: if not self_normalizing: - hidden_layer = tf.contrib.layers.fully_connected(hidden_layer, hidden_units, activation_fn = hidden_activation_function) + normalizer_fn = tf.layers.batch_normalization if batch_norm else None + hidden_layer = tf.contrib.layers.fully_connected(hidden_layer, hidden_units, activation_fn = hidden_activation_function, normalizer_fn = normalizer_fn) hidden_layer = tf.nn.dropout(hidden_layer, dropout_tensor) else: hidden_layer = tf.contrib.layers.fully_connected(hidden_layer, hidden_units, @@ -361,15 +362,15 @@ def mlp_base(hidden_activation_function = "relu"): return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 0, hidden_layers = [1000], self_normalizing = False, hidden_activation_function=hidden_activation_function, - standard_normal = False) + standard_normal = False, batch_norm = False) -def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, standard_normal, hidden_activation_function = "relu"): +def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, standard_normal, batch_norm, hidden_activation_function = "relu"): hidden_activation_function = _transform_activation_function(hidden_activation_function) return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, self_normalizing = self_normalizing, hidden_activation_function=hidden_activation_function, - standard_normal = standard_normal) + standard_normal = standard_normal, batch_norm = batch_norm) def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, dynamic_max_pooling_p): @@ -686,7 +687,9 @@ def fit(self, X, y): self.loss = self.label_loss # optimize - optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.gpu_memory_fraction) session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index f4f4061..d8a8e8b 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -591,6 +591,7 @@ def create_classifier(options, num_concepts): get_model = mlp_soph(options.dropout, options.embedding_size, hidden_layers = options.hidden_layers, self_normalizing = options.snn, standard_normal = options.standard_normal, + batch_norm = options.batch_norm, hidden_activation_function = options.hidden_activation_function ), patience = options.patience, @@ -848,6 +849,8 @@ def _generate_parsers(): the last layer of a particular model (e.g. CNN, LSTM) and the output layer. (None)") neural_network_options.add_argument('--standard_normal', action="store_true", dest="standard_normal", default=False, help= "Whether to normalize the input features to mean = 0 and std = 1 for MLPSoph. [False]") + neural_network_options.add_argument('--batch_norm', action="store_true", dest="batch_norm", default=False, help= + "Whether to apply batch normalization after at a hidden layer in MLP. [False]") neural_network_options.add_argument('--snn', action="store_true", dest="snn", default=False, help= "Whether to use SELU activation and -dropout. If set to False, the activation specified in --hidden_activation_function is used. [False]") neural_network_options.add_argument('--variational_recurrent_dropout', action="store_true", dest="variational_recurrent_dropout", default=False, help= From 3785c6b7868eebb29bbd9743458ce6bf60908e4d Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 14 Nov 2017 09:24:55 +0100 Subject: [PATCH 102/116] Options for choosing number of filters and window sizes for CNN. --- Code/lucid_ml/classifying/tensorflow_models.py | 15 +++++++++------ Code/lucid_ml/run.py | 8 +++++++- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index bd465d1..f3dc95c 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -184,7 +184,10 @@ def create_multilayer_lstm(): def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], pretrained_embeddings_path = None, trainable_embeddings = True, - dynamic_max_pooling_p = 1): + dynamic_max_pooling_p = 1, + # these are set according to Kim's Sentence Classification + window_sizes = [3, 4, 5], + num_filters = 100): """Model function for CNN.""" # x_tensor includes the max_index_column, feature_input doesnt. go on with feature_input, but return x_tensor for feed_dict @@ -210,9 +213,7 @@ def cnn_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [ # need to extend the number of dimensions here in order to use the predefined pooling operations, which assume 2d pooling embedded_words = tf.expand_dims(embedded_words, -1) - # these are set according to Kim's Sentence Classification - window_sizes = [3, 4, 5] - num_filters = 100 + stride = [1, 1, 1, 1] padding = "VALID" @@ -372,12 +373,14 @@ def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, hidden_activation_function=hidden_activation_function, standard_normal = standard_normal, batch_norm = batch_norm) -def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, dynamic_max_pooling_p): +def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, dynamic_max_pooling_p, window_sizes, num_filters): return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, trainable_embeddings = trainable_embeddings, - dynamic_max_pooling_p = dynamic_max_pooling_p) + dynamic_max_pooling_p = dynamic_max_pooling_p, + window_sizes = window_sizes, + num_filters = num_filters) def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout, bidirectional, aggregate_output, iterate_until_maxlength): diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index d8a8e8b..d02f54d 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -614,7 +614,9 @@ def create_classifier(options, num_concepts): hidden_layers = options.hidden_layers, pretrained_embeddings_path = options.pretrained_embeddings, trainable_embeddings=options.trainable_embeddings, - dynamic_max_pooling_p=options.dynamic_max_pooling_p), + dynamic_max_pooling_p=options.dynamic_max_pooling_p, + window_sizes = options.window_sizes, + num_filters = options.num_filters), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, gpu_memory_fraction = options.memory, @@ -868,6 +870,10 @@ def _generate_parsers(): "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= "Specify the number of chunks (p) to perform max-pooling over. [1]") + neural_network_options.add_argument('--num_filters', type=int, dest="num_filters", default=100, help= + "Specify the number of filters used in a CNN (per window size). [100]") + neural_network_options.add_argument('--window_sizes', type=int, dest="window_sizes", nargs='+', default=[3,4,5], help= + "Specify the window sizes used for extracting features in a CNN. [[3,4,5]]") neural_network_options.add_argument('--meta_labeler_min_labels', type=int, dest="meta_labeler_min_labels", default=1, help= "Specify the minimum number of labels to assign the meta labeler can predict. [1]") neural_network_options.add_argument('--meta_labeler_max_labels', type=int, dest="meta_labeler_max_labels", default=None, help= From 15cad8732e8248b96b639ceacd5536ca717ee11e Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 17 Dec 2017 11:48:44 +0100 Subject: [PATCH 103/116] Allow lists for grid search --- Code/lucid_ml/run.py | 30 ++++++++++++++++++++++++------ 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index d02f54d..f46b677 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -422,12 +422,30 @@ def _make_space(options): elif options.optimization == "grid": param_type = info[1] - if param_type == "int": - cast_func = int - elif param_type == "float": - cast_func = float - elif param_type == "string": - cast_func = str + def get_cast_func(some_string_type): + cast_func = None + if some_string_type == "int": + cast_func = int + elif some_string_type == "float": + cast_func = float + elif some_string_type == "string": + cast_func = str + elif some_string_type == "bool": + cast_func = bool + return cast_func + + cast_func = get_cast_func(param_type) + if cast_func is None: + if param_type.startswith("list"): + # determine type in list + list_type = get_cast_func(param_type.split("-")[1]) + + # assume they are seperated by semicolon + def extract_items(list_string): + return [list_type(x) for x in list_string.split(";")] + + cast_func = extract_items + # all possible values space[param_name] = list(map(cast_func, info[2:])) From 1065e98b0ab7560d29e95b7329e2ceef3d5dac54 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sat, 10 Feb 2018 18:03:01 +0100 Subject: [PATCH 104/116] Print label statistics when verbose. --- Code/lucid_ml/run.py | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index d02f54d..167a02e 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -67,7 +67,6 @@ def _build_features(options): persister = Persister(DATA_PATHS, options) if options.persist and persister.is_saved(): X, Y, tr = persister.read() - if VERBOSE: print("Y = " + str(Y.shape)) else: # --- LOAD DATA --- fold_list = None @@ -87,12 +86,6 @@ def _build_features(options): mlb = MultiLabelBinarizer(sparse_output=True, classes=[i[1] for i in sorted( tr.index_nodename.items())] if options.hierarch_f1 else None) Y = mlb.fit_transform(Y_raw) - - if VERBOSE: - print("Y = " + str(Y.shape)) - y_sum = Y.sum(axis = 0) - for i in range(1, 5): - print("Number of labels assigned more than", i, "times:" , np.sum(y_sum > i)) # --- EXTRACT FEATURES --- input_format = 'filename' if options.fulltext else 'content' @@ -207,6 +200,20 @@ def _print_feature_info(X, options): # n_iter = np.ceil(10**6 / (X.shape[0] * 0.9)) # print("Dynamic n_iter = %d" % n_iter) + +def _print_label_info(Y, VERBOSE): + if VERBOSE: + print("Y = " + str(Y.shape)) + y_sum = Y.sum(axis = 0) + for i in range(1, 5): + print("Number of labels assigned more than", i, "times:" , np.sum(y_sum > i)) + + # compute avg number of labels per document + sum_of_labels_per_document = Y.sum(axis = 1) + print("Average number of labels per document:" , np.mean(sum_of_labels_per_document)) + + # compute avg number of documents per label + print("Average number of documents per label:" , np.mean(y_sum)) def _check_interactive(options, X, Y, extractor, mlb): if options.interactive: @@ -465,6 +472,7 @@ def run(options): # load dataset and build feature representation X, Y, extractor, mlb, fold_list, X_raw, Y_raw, tr = _build_features(options) _print_feature_info(X, options) + _print_label_info(Y, options) # go to interactive mode if on _check_interactive(options, X, Y, extractor, mlb) From c220267452e5765ae2e56b0151d77ac43db3c300 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Sun, 11 Feb 2018 18:38:42 +0100 Subject: [PATCH 105/116] Documentation for two classes --- .../lucid_ml/classifying/tensorflow_models.py | 152 ++++++++++++------ Code/lucid_ml/utils/text_encoding.py | 27 +++- 2 files changed, 122 insertions(+), 57 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index f3dc95c..98164e2 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -358,6 +358,9 @@ def _transform_activation_function(func): return hidden_activation_function def mlp_base(hidden_activation_function = "relu"): + """ + Returns a function that can be used as the get_model parameter for MultiLabelSKFlow so it trains Base-MLP. + """ hidden_activation_function = _transform_activation_function(hidden_activation_function) return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 0, @@ -366,6 +369,9 @@ def mlp_base(hidden_activation_function = "relu"): standard_normal = False, batch_norm = False) def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, standard_normal, batch_norm, hidden_activation_function = "relu"): + """ + Returns a function that can be used as the get_model parameter for MultiLabelSKFlow so it trains MLP with the specified parameters. + """ hidden_activation_function = _transform_activation_function(hidden_activation_function) return lambda X, y : mlp_soph_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, @@ -374,7 +380,9 @@ def mlp_soph(keep_prob_dropout, embedding_size, hidden_layers, self_normalizing, standard_normal = standard_normal, batch_norm = batch_norm) def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, dynamic_max_pooling_p, window_sizes, num_filters): - + """ + Returns a function that can be used as the get_model parameter for MultiLabelSKFlow so it trains CNN with the specified configuration. + """ return lambda X, y : cnn_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, trainable_embeddings = trainable_embeddings, @@ -384,6 +392,9 @@ def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_ def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout, bidirectional, aggregate_output, iterate_until_maxlength): + """ + Returns a function that can be used as the get_model parameter for MultiLabelSKFlow so it trains LSTM with the specified configuration. + """ return lambda X, y : lstm_fn(X, y, keep_prob_dropout = keep_prob_dropout, embedding_size = embedding_size, hidden_layers = hidden_layers, pretrained_embeddings_path=pretrained_embeddings_path, @@ -395,7 +406,22 @@ def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings class BatchGenerator: - + """ + Splits a training/test set into batches to enable mini-batch training. + + Parameters + ---------- + X: np.array, or csr_matrix + The entire dataset to split into batches. + y: np.array, or csr_matrix + The corresponding goldstandard of the entire dataset to split into batches. May be None if 'predict' = False + batch_size: int + Size of the resulting batches. + shuffle: bool + Whether to shuffle the dataset before splitting into batches (should be done at training time, shouldn't be done at prediction time). + predict: bool + Whether we are in prediction mode, i.e., there is no goldstandard. + """ def __init__(self, X, y, batch_size, shuffle, predict): self.X = X self.y = y @@ -432,15 +458,79 @@ def _batch_generator(self): class MultiLabelSKFlow(BaseEstimator): """ - This is a wrapper class for tf.contrib.learn.Estimators, so it adheres to the fit/predict naming conventions of sk-learn. - It already handles mini-batching, whose behavior can be controlled by providing the respective parameters to the init function. - - The concrete TensorFlow model to execute can be specified in terms of the 'get_model' function. - This function in turn has to return a 'model_fn' function and a 'params' dictionary. - These arguments will passed to the tf.contrib.learn. Estimator class and have thus to conform the formats - described in 'https://www.tensorflow.org/extend/estimators'. - On top of that, 'model_fn' has to accept an additional, non-positional argument 'num_classes' which is used to - infer the output size. Furthermore, the function has to assume the 'features' and 'targets' parameters to be of the Tensor class. + This is a wrapper class for TensorFlow, so it adheres to the fit/predict naming conventions of sk-learn. + This class handles the output layer, mini-batch learning, early stopping, threshold optimization on the validation set, and the neural metalabeler. + + The concrete TensorFlow model up to the last hidden layer can be specified in terms of the 'get_model' function. + This function in turn has to accept the dataset X (np.array, or csr_matrix), and the goldstandard y (csr_matrix). + Moreover, get_model() is expected to return the following components: + + x_tensor: tf.placeholder + Used to pass input data to the model at training and test time. + y_tensor: tf.placeholder + Used by to pass the ground truth to the model during training. + last_layer: tf.Tensor + The TensorFlow computation graph from input layer to last hidden layer of the implemented neural network. + params_fit: dictionary + Parameters to be added to the feed dictionary for training (e.g., keep_probability_placeholder -> 0.5) + params_predict: dictionary + Parameters to be added to the feed dictionary + at prediction time (e.g., keep_probability_placeholder -> 1.0) + initializer_operations: list of (tf.Tensor, dictionary) + A list of + pairs consisting of operations for initializing variables (e.g., + embedding tables) before training starts, and the feed dictionary with data to execute + the initialize operation. + + Moreover, training can be controlled by the following parameters: + + Parameters + ---------- + batch_size: int, default = 5 + Batch size to use during training and at prediction time. + num_epochs: int, default = 10 + Number of iterations over the dataset during training. + get_model: function, default = mlp_base() + The function that returns the underlying neural network up to the last hidden layer. See above description. + threshold: float, default = 0.2 + Fixed threshold to use if "optimize_threshold" = False, or starting threshold when "optimize_threshold" = True. + learning_rate: float, default = 0.1 + Initial learning rate to use for Adam. + patience, int, default = 5 + Number of non-improving evaluations on the validation set before terminating training. + validation_metric, function true_values, predicted_values -> float, default = f1_score + The metric that is used for evaluating prediction on the validation set. + optimize_threshold, boolean, default = True + Determines whether the threshold is optimized on a validation set. + threshold_window, array-like of float, default = np.linspace(-0.03, 0.03, num=7) + An array of floats that are interpreted as offset from the current threshold value. When optimizing the threshold, + each of these offsets is added to the current threshold and the validation performance is assessed. Afterwards, the + threshold is set to the value that has yielded the best score. + tf_model_path, str, default = ".tmp_best_models" + A path to the folder where the weights of the best model are saved, so it can be loaded at prediction time. + num_steps_before_validation, int, default = None + Determines the number of batches between two performance evaluations on the validation set. If set to None, this number is determined from the size of + the training set, i.e., it is set to one epoch. + hidden_activation_function, TensorFlow operation, default = tf.nn.relu + The activation function to apply after the bottleneck layer. + bottleneck_layers, list of int, default = None + As many layers as there are elements in this list are injected before the output layer. Element i specifies the number of units + in bottleneck layer i. + hidden_keep_prob, float, default = 0.5 + Specifies the keep probability of dropout to apply after each bottleneck layer. + gpu_memory_fraction, float, default = 1. + Specifies how much of the RAM of each available GPU TensorFlow may reserve. + meta_labeler_phi, str, default = None + Determines which 'phi' function from the definition of Neural MetaLabeler we use: "content", "score", or None. If none is used, MetaLabeler is not + used at all. If "content" is used, the prediction is based on the output of the last hidden layer from the underlying neural network (given by get_model). + If "score" is used, the prediction is based on the probabilities given by the output layer. + meta_labeler_alpha, float, default = 0.1 + The label-classification objective is weighted by (1 - alpha), and the objective of predicting the number of labels is weighted by alpha. + meta_labeler_min_labels, int, default = 1 + Specifies the smallest possible number of labels that can be predicted by Neural MetaLabeler. + meta_labeler_max_labels, int, default = None + Specifies the largest possible number of labels that can be predicted by Neural MetaLabeler. If set to None, the maximum number of labels is determined from + the training set. """ def __init__(self, batch_size = 5, num_epochs = 10, get_model = mlp_base(), threshold = 0.2, learning_rate = 0.1, patience = 5, @@ -524,7 +614,6 @@ def _make_binary_decision(self, predictions): if self.meta_labeler_phi is None: y_pred = predictions > self.threshold else: - #TODO: implement meta-labeler label assignment predictions, meta_labeler_predictions = predictions max_probability_cols = np.argmax(meta_labeler_predictions, axis = 1) max_probability_indices = tuple(np.indices([meta_labeler_predictions.shape[0]]))+(max_probability_cols,) @@ -635,8 +724,6 @@ def fit(self, X, y): # Remove previous weights, bias, inputs, etc.. tf.reset_default_graph() tf.set_random_seed(1337) - - # Inputs # get_model has to return a self.x_tensor, self.y_tensor, self.last_layer, self.params_fit, self.params_predict, initializer_operations = self.get_model(X, y) @@ -804,36 +891,6 @@ def fit(self, X, y): print("Training of TensorFlow model finished!") print("Longest sequence of epochs of no improvement:", most_consecutive_epochs_with_no_improvement) - - #def model_fn_with_num_classes(features, targets, mode, params): - # return self.model_fn(features, targets, mode, params, y.shape[1]) - - #self._estimator = tf.contrib.learn.Estimator(model_fn=model_fn_with_num_classes, params=self.model_params) - - #======================================================================= - # val_pos = self.validation_data_position - # monitors = [] - # if val_pos is not None: - # #callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')) - # #callbacks.append(ModelCheckpoint("weights.best.hdf5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')) - # X_train, y_train, X_val, y_val = X[:val_pos, :], y[:val_pos,:], X[val_pos:, :], y[val_pos:,:] - # - # validation_batch_generator = BatchGenerator(X_val, y_val, X_val.shape[0], False, False) - # steps_per_epoch = int(np.ceil(X_train.shape[0] / self.batch_size)) - # monitors.append(tf.contrib.learn.monitors.ValidationMonitor(input_fn = validation_batch_generator._batch_generator, - # every_n_steps=steps_per_epoch, - # early_stopping_metric="loss", - # early_stopping_rounds=5 * steps_per_epoch, - # eval_steps = int(np.ceil(X_val.shape[0] / self.batch_size)))) - # else: - # steps_per_epoch = int(np.ceil(X.shape[0] / self.batch_size)) - # X_train = X - # y_train = y - # - # batch_generator = BatchGenerator(X_train, y_train, self.batch_size, True, False) - # - # self._estimator.fit(input_fn= batch_generator._batch_generator, steps=self.num_epochs * steps_per_epoch, monitors = monitors) - #======================================================================= def predict(self, X): @@ -844,13 +901,6 @@ def predict(self, X): # Load model loader = tf.train.import_meta_graph(self._save_model_path + '.meta') loader.restore(self.session, self._save_model_path) - - # Get Tensors from loaded model - #loaded_x = loaded_graph.get_tensor_by_name('x:0') - #loaded_y = loaded_graph.get_tensor_by_name('y:0') - #loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0') - #loaded_logits = loaded_graph.get_tensor_by_name('logits:0') - #loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0' prediction = np.zeros((X.shape[0], self.y.shape[1])) batch_generator = BatchGenerator(X, None, self.batch_size, False, True) diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index be1ff6f..392255a 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -5,10 +5,29 @@ import numpy as np - - class TextEncoder(BaseEstimator, TransformerMixin): + """ + Sk-learn transformer that turns raw text into a fixed-length sequence of one-hot vectors. + Length of the sequence is either set by the user or the length of the longest sample that is + passed to the fit() function. If a sequence is shorter than the maximum length, it is padded with zeros. + If it is longer, the text is truncated to the maximum length. + + If 'pretrained' is specified, it is interpreted as a word embedding file (word2vec format). Any token that does not have an entry in the + embedding table is discarded. + Parameters + ---------- + tokenize: function, default = NltkNormalizer().split_and_normalize + Function that turns a raw string into a sequence of tokens. + input_format: str, default = content + Determines whether the samples passed to fit() or transform() are processed directly ("content") or if they are interpreted as a path ("filename"). + max_words: int, default = None + Determines the maximum sequence length. If None, the maximum sequence length is determined from the samples passed to fit(). + pretrained: str, default = None + Path to pretrained word embeddings. + restrict_pretrained: bool, default = True + If true, a word embedding table is generated that only contains those words which appear among the samples. + """ def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", @@ -76,10 +95,6 @@ def _set_of_all_words(self, X): return all_words def fit(self, X, y = None): - """ - X is expected to be a list of either strings, which represent the text directly, or paths to the - file where to find the text. - """ if self.pretrained is None: mapping = {} From d0c92c12c24eaa867392589ef28f2670ebde924c Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 2 Mar 2018 11:08:18 +0100 Subject: [PATCH 106/116] Implement OE-LSTMs but not iterate until maxlength but for a set number of tokens --- .../lucid_ml/classifying/tensorflow_models.py | 2 +- Code/lucid_ml/run.py | 7 +++++-- Code/lucid_ml/utils/text_encoding.py | 19 +++++++++++++++++-- 3 files changed, 23 insertions(+), 5 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index 98164e2..c5e1eb6 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -15,7 +15,7 @@ def _load_embeddings(filename, vocab_size, embedding_size): - embeddings = np.zeros((vocab_size, embedding_size)) + embeddings = np.random.normal(scale = 0.1, size = (vocab_size, embedding_size)) with open(filename + ".tmp",'r') as embedding_file: i = 0 diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index f57a523..47d7d35 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -156,7 +156,8 @@ def _build_features(options): features.append(("char_ngrams", character_ngrams)) if options.onehot: features.append(("onehot", TextEncoder(input_format = "filename" if options.fulltext else "content", - max_words=options.max_features, pretrained = options.pretrained_embeddings))) + max_words=options.max_features, pretrained = options.pretrained_embeddings, + pad_special_symbol=options.pad_special_symbol))) if len(features) == 0: raise ValueError("No feature representation specified!") @@ -889,9 +890,11 @@ def _generate_parsers(): neural_network_options.add_argument('--iterate_until_maxlength', action="store_true", dest="iterate_until_maxlength", default=False, help= "When activated, the LSTM always iterates max_features steps, even if the actual sequence is shorter. Instead, it consumes\ at each additional step the padding symbol. The outputs of steps beyond the actual sequence length are taken into account as well for output aggregation. [False]") - detailed_options.add_argument('--aggregate_output', type=str, dest='aggregate_output', default="average", help= + neural_network_options.add_argument('--aggregate_output', type=str, dest='aggregate_output', default="average", help= "How to aggregate the outputs of an LSTM. 'last' uses the output at the last time step. 'average' takes the mean over all outputs. [average]", choices = ["average", "last", "attention", "sum"]) + neural_network_options.add_argument('--pad_special_symbol', type=int, dest="pad_special_symbol", default=0, help= + "How many special tokens to pad after each sample for OE-LSTMs. [0]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= "Optimize the prediction threshold on validation set during training. [False]") neural_network_options.add_argument('--dynamic_max_pooling_p', type=int, dest="dynamic_max_pooling_p", default=1, help= diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index 392255a..367c711 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -27,19 +27,23 @@ class TextEncoder(BaseEstimator, TransformerMixin): Path to pretrained word embeddings. restrict_pretrained: bool, default = True If true, a word embedding table is generated that only contains those words which appear among the samples. + pad_special_symbol: int, default = 0 + Number of times to repeat a special symbol. """ def __init__(self, tokenize = NltkNormalizer().split_and_normalize, input_format = "content", max_words = None, pretrained = None, - restrict_pretrained = True): + restrict_pretrained = True, + pad_special_symbol = 0): self.tokenize = tokenize self.input = input_format self.max_words = max_words self.pretrained = pretrained self.restrict_pretrained = restrict_pretrained + self.pad_special_symbol = pad_special_symbol def _maybe_load_text(self, text): if self.input == "filename": @@ -132,6 +136,11 @@ def fit(self, X, y = None): if len(words) > max_length: max_length = len(words) + # need to account for the special symbol + if self.pad_special_symbol > 0: + max_index += 1 + max_length = max_length + self.pad_special_symbol + # save variables required for transformation step self.mapping = mapping self.max_index = max_index - 1 @@ -149,11 +158,17 @@ def transform(self, X, y = None): # tokenize test text words = self.tokenize(text) # make sure we do not exceed the maximum length from training samples - words = self._limit_num_words(words, self.max_length) + words = self._limit_num_words(words, self.max_length - self.pad_special_symbol) # apply mapping from word to integer id_sequence = np.array([self.mapping[word] for word in words if word in self.mapping]) + # add special padding token if necessary + if self.pad_special_symbol > 0: + padding_sequence = np.array([self.max_index for _ in range(self.pad_special_symbol)]) + id_sequence = np.concatenate((id_sequence, padding_sequence)) + print(id_sequence) + encoding_matrix[i, :len(id_sequence)] = id_sequence max_index_column = np.zeros((len(X), 1), dtype = np.int32) From 56332ee9fe2cff825b57e7515cd30733039b72e0 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 2 Mar 2018 11:28:04 +0100 Subject: [PATCH 107/116] Remove unneccessary printout --- Code/lucid_ml/utils/text_encoding.py | 1 - 1 file changed, 1 deletion(-) diff --git a/Code/lucid_ml/utils/text_encoding.py b/Code/lucid_ml/utils/text_encoding.py index 367c711..b8a6048 100644 --- a/Code/lucid_ml/utils/text_encoding.py +++ b/Code/lucid_ml/utils/text_encoding.py @@ -167,7 +167,6 @@ def transform(self, X, y = None): if self.pad_special_symbol > 0: padding_sequence = np.array([self.max_index for _ in range(self.pad_special_symbol)]) id_sequence = np.concatenate((id_sequence, padding_sequence)) - print(id_sequence) encoding_matrix[i, :len(id_sequence)] = id_sequence From c4915c6b1da38c6df7bbd0a4475d589433827131 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Fri, 2 Mar 2018 16:52:37 +0100 Subject: [PATCH 108/116] OverEager attention: attention only over overeager outputs. --- .../lucid_ml/classifying/tensorflow_models.py | 41 ++++++++++++------- Code/lucid_ml/run.py | 5 ++- 2 files changed, 29 insertions(+), 17 deletions(-) diff --git a/Code/lucid_ml/classifying/tensorflow_models.py b/Code/lucid_ml/classifying/tensorflow_models.py index c5e1eb6..0f08f01 100644 --- a/Code/lucid_ml/classifying/tensorflow_models.py +++ b/Code/lucid_ml/classifying/tensorflow_models.py @@ -87,13 +87,30 @@ def extract_axis_1(data, ind): return res +def _word_attention(output_state): + # we perform attention according to Hierarchical Attention Network (word attention) + ## compute hidden representation of outputs + ### context vector size as in HN-ATT + context_vector_size = 100 + hidden_output_representation = tf.contrib.layers.fully_connected(output_state, context_vector_size, activation_fn = tf.nn.tanh) + ## compute dot product with context vector + context_vector = tf.Variable(tf.random_normal([context_vector_size], stddev=0.1)) + dot_product = tf.tensordot(hidden_output_representation, context_vector, [[2], [0]]) + ## compute weighted sum + attention_weights = tf.nn.softmax(dot_product) + attention_weights = tf.expand_dims(attention_weights, -1) + output_state = tf.reduce_sum(tf.multiply(output_state, attention_weights), axis = 1) + + return output_state + def lstm_fn(X, y, keep_prob_dropout = 0.5, embedding_size = 30, hidden_layers = [1000], aggregate_output = "average", pretrained_embeddings_path = None, trainable_embeddings = True, variational_recurrent_dropout = True, bidirectional = False, - iterate_until_maxlength = False): + iterate_until_maxlength = False, + num_last_outputs = 1): """Model function for LSTM.""" x_tensor, vocab_size, feature_input = _extract_vocab_size(X) @@ -163,18 +180,11 @@ def create_multilayer_lstm(): # return output at last time step output_state = extract_axis_1(output_state, seq_length - 1) elif aggregate_output == "attention": - # we perform attention according to Hierarchical Attention Network (word attention) - ## compute hidden representation of outputs - ### context vector size as in HN-ATT - context_vector_size = 100 - hidden_output_representation = tf.contrib.layers.fully_connected(output_state, context_vector_size, activation_fn = tf.nn.tanh) - ## compute dot product with context vector - context_vector = tf.Variable(tf.random_normal([context_vector_size], stddev=0.1)) - dot_product = tf.tensordot(hidden_output_representation, context_vector, [[2], [0]]) - ## compute weighted sum - attention_weights = tf.nn.softmax(dot_product) - attention_weights = tf.expand_dims(attention_weights, -1) - output_state = tf.reduce_sum(tf.multiply(output_state, attention_weights), axis = 1) + output_state = _word_attention(output_state) + elif aggregate_output == "oe-attention": + # perform attention over overeager outputs + output_state = tf.concat([extract_axis_1(output_state, seq_length - (num_last_outputs + 1 - i)) for i in range(num_last_outputs)], axis = 1) + _word_attention(output_state) else: raise ValueError("Aggregation method not implemented!") @@ -391,7 +401,7 @@ def cnn(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_ num_filters = num_filters) def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings_path, trainable_embeddings, variational_recurrent_dropout, - bidirectional, aggregate_output, iterate_until_maxlength): + bidirectional, aggregate_output, iterate_until_maxlength, num_last_outputs): """ Returns a function that can be used as the get_model parameter for MultiLabelSKFlow so it trains LSTM with the specified configuration. """ @@ -402,7 +412,8 @@ def lstm(keep_prob_dropout, embedding_size, hidden_layers, pretrained_embeddings variational_recurrent_dropout=variational_recurrent_dropout, bidirectional = bidirectional, aggregate_output = aggregate_output, - iterate_until_maxlength = iterate_until_maxlength) + iterate_until_maxlength = iterate_until_maxlength, + num_last_outputs=num_last_outputs) class BatchGenerator: diff --git a/Code/lucid_ml/run.py b/Code/lucid_ml/run.py index 47d7d35..7d87ae4 100644 --- a/Code/lucid_ml/run.py +++ b/Code/lucid_ml/run.py @@ -665,7 +665,8 @@ def create_classifier(options, num_concepts): variational_recurrent_dropout = options.variational_recurrent_dropout, bidirectional = options.bidirectional, aggregate_output = options.aggregate_output, - iterate_until_maxlength = options.iterate_until_maxlength), + iterate_until_maxlength = options.iterate_until_maxlength, + num_last_outputs = options.pad_special_symbol), bottleneck_layers = options.bottleneck_layers, hidden_keep_prob = options.dropout, gpu_memory_fraction = options.memory, @@ -892,7 +893,7 @@ def _generate_parsers(): at each additional step the padding symbol. The outputs of steps beyond the actual sequence length are taken into account as well for output aggregation. [False]") neural_network_options.add_argument('--aggregate_output', type=str, dest='aggregate_output', default="average", help= "How to aggregate the outputs of an LSTM. 'last' uses the output at the last time step. 'average' takes the mean over all outputs. [average]", - choices = ["average", "last", "attention", "sum"]) + choices = ["average", "last", "attention", "oe-attention", "sum"]) neural_network_options.add_argument('--pad_special_symbol', type=int, dest="pad_special_symbol", default=0, help= "How many special tokens to pad after each sample for OE-LSTMs. [0]") neural_network_options.add_argument('--optimize_threshold', action="store_true", dest="optimize_threshold", default=False, help= From 1c7f1e90f09508f1424cd66d5a308e7449a4f304 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 15 Mar 2018 15:44:18 +0100 Subject: [PATCH 109/116] Updated readme and added configuration files for reproducability. --- Code/lucid_ml/file_paths.json | 26 +++------ Experiments/cnn_final_experiments.cfg | 17 ++++++ Experiments/final_base_mlp_experiments.cfg | 17 ++++++ Experiments/final_mlp_experiments.cfg | 17 ++++++ Experiments/lstm_experiments_final.cfg | 17 ++++++ LICENSE | 4 +- README.md | 64 +++------------------- 7 files changed, 87 insertions(+), 75 deletions(-) create mode 100644 Experiments/cnn_final_experiments.cfg create mode 100644 Experiments/final_base_mlp_experiments.cfg create mode 100644 Experiments/final_mlp_experiments.cfg create mode 100644 Experiments/lstm_experiments_final.cfg diff --git a/Code/lucid_ml/file_paths.json b/Code/lucid_ml/file_paths.json index 14b4ce2..09d27c4 100644 --- a/Code/lucid_ml/file_paths.json +++ b/Code/lucid_ml/file_paths.json @@ -1,22 +1,12 @@ { - "example-titles": { - "X": "../../Resources/example/example-titles.tsv", - "y": "../../Resources/example/example-goldstandard.tsv", - "thes": "../../Resources/example/stw.json" - }, - "example-fulltext": { - "X": "../../Resources/example/example-fulltext", - "y": "../../Resources/example/example-goldstandard.tsv", - "thes": "../../Resources/example/stw.json" + "econbiz": { + "format" : "combined", + "X" : "../../Resources/econbiz.csv", + "label_delimiter" : "\t" }, - "econtitles": { + "pubmed": { "format" : "combined", - "X" : "../../Resources/example/data_sample.csv", - "thes": "../../Resources/example/stw.json" - }, - "econtitles-full": { -"format" : "combined", -"X" : "../../../Economics/data_full.csv", -"thes": "../../Resources/example/stw.json" -} + "X" : "../../Resources/pubmed.csv", + "label_delimiter" : "\t" + } } diff --git a/Experiments/cnn_final_experiments.cfg b/Experiments/cnn_final_experiments.cfg new file mode 100644 index 0000000..d439067 --- /dev/null +++ b/Experiments/cnn_final_experiments.cfg @@ -0,0 +1,17 @@ +# these experiments are used to compute the final scores +# singlefold: +# python run.py -f cnn --onehot -k file_paths.json --fixed_folds --folds=1 -v --batch_size=256 -e 200 --val-size=0.2 -C ../../Experiments/cnn_final_experiments.cfg -o cnn_final_experiments_singlefold.csv --optimize_threshold --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=250 --num_steps_before_validation=2000 --embedding_size=300 --pretrained_embeddings=<path/to/embedding/file/in/w2v/format> --trainable_embeddings --learning_rate=0.001 --dropout=0.75 --window_sizes 2 3 4 5 8 +# 10-fold-crossvalidation: +# python run.py -f cnn --onehot -k file_paths_ks.json --fixed_folds --folds=10 -v --batch_size=256 -e 200 --val-size=0.2 -C ../../Experiments/cnn_final_experiments.cfg -o cnn_final_experiments.csv --optimize_threshold --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=250 --num_steps_before_validation=2000 --embedding_size=300 --pretrained_embeddings=<path/to/embedding/file/in/w2v/format> --trainable_embeddings --learning_rate=0.001 --dropout=0.75 --window_sizes 2 3 4 5 8 +# PubMed +--extra_samples_factor=1 -K pubmed --dynamic_max_pooling_p=1 --bottleneck_layers 500 --num_filters=400 +--extra_samples_factor=2 -K pubmed --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=4 -K pubmed --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=8 -K pubmed --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=100 -K pubmed --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +# EconBiz +--extra_samples_factor=1 -K econbiz --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=2 -K econbiz --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=4 -K econbiz --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=8 -K econbiz --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 +--extra_samples_factor=100 -K econbiz --dynamic_max_pooling_p=1 --bottleneck_layer 500 --num_filters=400 diff --git a/Experiments/final_base_mlp_experiments.cfg b/Experiments/final_base_mlp_experiments.cfg new file mode 100644 index 0000000..896c5f2 --- /dev/null +++ b/Experiments/final_base_mlp_experiments.cfg @@ -0,0 +1,17 @@ +# experiments for final evaluation with increasing samples factors for base mlp +# for single fold: +# python run.py -f basemlp -t -k file_paths.json --fixed_folds --folds=1 -v --batch_size=256 -e 200 --val-size=0.2 --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=25000 --num_steps_before_validation=2000 --learning_rate=0.001 --dropout=0.5 --memory=0.3 -C ../../Experiments/final_base_mlp_experiments.cfg -o final_base_mlp_singlefold.csv --embedding_size=0 --optimize_threshold +# for cross-validation: +# python run.py -f basemlp -t -k file_paths_ks.json --fixed_folds --folds=10 -v --batch_size=256 -e 200 --val-size=0.2 --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=25000 --num_steps_before_validation=2000 --learning_rate=0.001 --dropout=0.5 --memory=0.3 -C ../../Experiments/final_base_mlp_experiments.cfg -o final_base_mlp_crossvalidated.csv --embedding_size=0 --optimize_threshold +# EconBiz +--extra_samples_factor=1 -K econbiz +--extra_samples_factor=2 -K econbiz +--extra_samples_factor=4 -K econbiz +--extra_samples_factor=8 -K econbiz +--extra_samples_factor=100 -K econbiz +# PubMed +--extra_samples_factor=1 -K pubmed +--extra_samples_factor=2 -K pubmed +--extra_samples_factor=4 -K pubmed +--extra_samples_factor=8 -K pubmed +--extra_samples_factor=100 -K pubmed diff --git a/Experiments/final_mlp_experiments.cfg b/Experiments/final_mlp_experiments.cfg new file mode 100644 index 0000000..ccdde96 --- /dev/null +++ b/Experiments/final_mlp_experiments.cfg @@ -0,0 +1,17 @@ +# experiments for final evaluation with increasing samples factors +# for single fold: +# python run.py -f mlpsoph -t -k file_paths.json --fixed_folds --folds=1 -v --batch_size=256 -e 200 --val-size=0.2 --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=25000 --num_steps_before_validation=2000 --learning_rate=0.001 --dropout=0.5 --memory=0.3 -C ../../Experiments/final_mlp_experiments.cfg -o final_mlp.csv --embedding_size=0 --ngram_limit=2 --optimize_threshold +# for cross-validation: +# python run.py -f mlpsoph -t -k file_paths_ks.json --fixed_folds --folds=10 -v --batch_size=256 -e 200 --val-size=0.2 --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=25000 --num_steps_before_validation=2000 --learning_rate=0.001 --dropout=0.5 --memory=0.3 -C ../../Experiments/final_mlp_experiments.cfg -o final_mlp.csv --embedding_size=0 --ngram_limit=2 --optimize_threshold +# EconBiz +--extra_samples_factor=1 -K econbiz --hidden_layers 2000 +--extra_samples_factor=2 -K econbiz --hidden_layers 2000 +--extra_samples_factor=4 -K econbiz --hidden_layers 2000 +--extra_samples_factor=8 -K econbiz --hidden_layers 2000 +--extra_samples_factor=100 -K econbiz --hidden_layers 2000 +# PubMed +--extra_samples_factor=1 -K pubmed --hidden_layers 1000 1000 --batch_norm --dropout=1. +--extra_samples_factor=2 -K pubmed --hidden_layers 1000 1000 --batch_norm --dropout=1. +--extra_samples_factor=4 -K pubmed --hidden_layers 1000 1000 --batch_norm --dropout=1. +--extra_samples_factor=8 -K pubmed --hidden_layers 1000 1000 --batch_norm --dropout=1. +--extra_samples_factor=100 -K pubmed --hidden_layers 1000 1000 --batch_norm --dropout=1. diff --git a/Experiments/lstm_experiments_final.cfg b/Experiments/lstm_experiments_final.cfg new file mode 100644 index 0000000..2ef7a76 --- /dev/null +++ b/Experiments/lstm_experiments_final.cfg @@ -0,0 +1,17 @@ +# experiments for final run of LSTM +# single fold +# python run.py -f lstm --onehot -k file_paths.json --fixed_folds --folds=1 -v --batch_size=256 -e 200 --val-size=0.2 -C ../../Experiments/lstm_experiments_final.cfg -o lstm_experiments_final_singlefold.csv --optimize_threshold --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=250 --num_steps_before_validation=2000 --embedding_size=300 --pretrained_embeddings=<path/to/embedding/file/in/w2v/format> --trainable_embeddings --bidirectional --aggregate_output=attention +# crossvalidation +# # python run.py -f lstm --onehot -k file_paths_ks.json --fixed_folds --folds=10 -v --batch_size=256 -e 200 --val-size=0.2 -C ../../Experiments/lstm_experiments_final.cfg -o lstm_experiments_final_crossval.csv --optimize_threshold --patience=10 --tf-model-path=<path/to/temporary/folder> --max_features=250 --num_steps_before_validation=2000 --embedding_size=300 --pretrained_embeddings=<path/to/embedding/file/in/w2v/format> --trainable_embeddings --bidirectional --aggregate_output=attention +# PubMed +-K pubmed --extra_samples_factor=1 --learning_rate=0.001 --dropout=0.75 --hidden_layers 1536 +-K pubmed --extra_samples_factor=2 --learning_rate=0.001 --dropout=0.75 --hidden_layers 1536 +-K pubmed --extra_samples_factor=4 --learning_rate=0.001 --dropout=0.75 --hidden_layers 1536 +-K pubmed --extra_samples_factor=8 --learning_rate=0.001 --dropout=0.75 --hidden_layers 1536 +-K pubmed --extra_samples_factor=100 --learning_rate=0.001 --dropout=0.75 --hidden_layers 1536 +# EconBiz +-K econbiz --extra_samples_factor=1 --learning_rate=0.001 --dropout=0.5 --hidden_layers 1536 +-K econbiz --extra_samples_factor=2 --learning_rate=0.001 --dropout=0.5 --hidden_layers 1536 +-K econbiz --extra_samples_factor=4 --learning_rate=0.001 --dropout=0.5 --hidden_layers 1536 +-K econbiz --extra_samples_factor=8 --learning_rate=0.001 --dropout=0.5 --hidden_layers 1536 +-K econbiz --extra_samples_factor=100 --learning_rate=0.001 --dropout=0.5 --hidden_layers 1536 diff --git a/LICENSE b/LICENSE index c6779c1..a68397a 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,4 @@ -Copyright (c) 2016, Dennis Brunsch, Lukas Galke, Florian Mai, Alan Schelten, Kiel University +Copyright (c) 2016-2018, Dennis Brunsch, Lukas Galke, Florian Mai, Alan Schelten, Kiel University All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: @@ -9,4 +9,4 @@ Redistribution and use in source and binary forms, with or without modification, 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 3df23f1..a01d59c 100644 --- a/README.md +++ b/README.md @@ -1,53 +1,9 @@ -# Quadflor +# Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text -![Multi-label classification pipeline](Documentation/graphics/pipelineExtended.png) +This repository contains the code for the JCDL paper [Using Deep Learning For Title-Based Semantic Subject Indexing To Reach Competitive Performance to Full-Text](https://arxiv.org/abs/1801.06717). It is based on and extents the multi-label classification framework [Quadflor](https://github.com/quadflor/Quadflor). ## Multi-label classification -The multi-label classification task consists of assigning a set of class labels -to a sample of subjects. In the context of scientific or news documents, the -task is equivalent to subject indexing. Using machine learning, the goal is to -generalize from a training set of labeled documents to unlabeled documents. - -## A text classification pipeline - -[Quadflor](https://github.com/quadflor/Quadflor) is a text-processing pipeline -for multi-label classification of documents and its evaluation. Given -a domain-specific thesaurus with descriptor labels, the different algorithms -learn how to assign these labels to documents from a training set. The -framework supports opportunities to conduct concept extraction, synonym set -resolution, spreading activation including hierarchical re-weighting. The -further processing of these features is performed by a classifier. As built-in -classifier options, Quadflor provides: - -- Naive Bayes (two variants) -- Logistic Regression -- Linear Support Vector Machine -- K-Nearest Neighbors (two multi-label adaption variants, as well as Rocchio), -- Stochastic Gradient Descent -- Stacked Decision Tree Classifier -- Learning2Rank -- Multilayer Perceptron (MLP) - -While the text-processing pipeline is designed for automatic -evaluation of novel classification strategies, it can also be employed in -a practical setting using all known documents as training data and classifying -new, unknown documents. - -## Experimental results - -In our [experiments](http://arxiv.org/abs/1705.05311), we compared several configurations of -classification techniques using either the full-text or only the titles of the -documents as input. We found that stochastic gradient descent (SGD) optimizing -logistic loss yields reasonably good classification results with respect to -the training time. SGD attained .485 sample-averaged F1 score on the full-text, .429 on the titles -of our economics data set. When more training time can be afforded, decision -trees on top of the SGD classifier could even raise the performance: .498 on -full-text, .451 on titles. Finally, the Multilayer Perceptron requires the -most training time and leads to the best results: .519 on full-text, .472 on -titles. - - ## Installation Install Python 3.4 or higher and @@ -63,14 +19,12 @@ cd Code pip install -r requirements.txt ``` -## Example Usage - -An example call with tf-idf features and stochastic gradient descent classifier: +## Replicating the results -```sh -cd Code/lucid_ml -python3 run.py -tf sgd -k Code/lucid_ml/file_paths.json -K example-titles --interactive -``` +In order to enhance the reproducability of our study, we uploaded a copy of the title datasets to Kaggle. Moreover, we provide the configurations used to produce the results from the paper. -where `file_paths.json` should contain the key given by `-K` specifying the -paths to data (`X`), the gold standard (`y`), and the thesaurus (`thes`). +To rerun any of the (title) experiments, do the following: +0. Download the [econbiz.csv](www.kaggle.com) and [pubmed.csv](www.kaggle.com) file, respectively, and copy them to the folder *Resources*. +2. Open the .cfg file of the respective method that you want to run (MLP, BaseMLP, CNN, or LSTM) from the *Experiments* folder. Copy the command in the third (if you want to evaluate on a single fold) or fifth (if you want to do a full 10-fold-cross-validation) line. +4. In the command, adjust the parameter for the option --tf-model-path parameter (specifies where to save the weights of the models, which can be gigabytes, so make sure you have enough disk space), and the --pretrained_embeddings parameter to the location of the GloVe model in your environment. +5. *cd* to the folder *Code/lucid_ml* and run the command. From 9c1c67ef895e32813638799044c8b9bf6618b976 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Thu, 15 Mar 2018 16:13:47 +0100 Subject: [PATCH 110/116] Updated requirements --- Code/requirements.txt | 4 +++- README.md | 2 -- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Code/requirements.txt b/Code/requirements.txt index f4addec..69165c6 100644 --- a/Code/requirements.txt +++ b/Code/requirements.txt @@ -8,6 +8,8 @@ nltk datrie decorator pandas -tensorflow +tensorflow==1.4 keras h5py +bayesian-optimization +hyperopt diff --git a/README.md b/README.md index a01d59c..2289628 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,6 @@ This repository contains the code for the JCDL paper [Using Deep Learning For Title-Based Semantic Subject Indexing To Reach Competitive Performance to Full-Text](https://arxiv.org/abs/1801.06717). It is based on and extents the multi-label classification framework [Quadflor](https://github.com/quadflor/Quadflor). -## Multi-label classification - ## Installation Install Python 3.4 or higher and From 2cc0984f7094244e18b911803e1129629d3d3519 Mon Sep 17 00:00:00 2001 From: Florian Mai <stu96542@mail.uni-kiel.de> Date: Tue, 3 Apr 2018 09:49:52 +0200 Subject: [PATCH 111/116] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2289628..ac3ca1b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text -This repository contains the code for the JCDL paper [Using Deep Learning For Title-Based Semantic Subject Indexing To Reach Competitive Performance to Full-Text](https://arxiv.org/abs/1801.06717). It is based on and extents the multi-label classification framework [Quadflor](https://github.com/quadflor/Quadflor). +This repository contains the code for the JCDL paper [Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text](https://arxiv.org/abs/1801.06717). It is based on and extents the multi-label classification framework [Quadflor](https://github.com/quadflor/Quadflor). ## Installation @@ -22,7 +22,7 @@ pip install -r requirements.txt In order to enhance the reproducability of our study, we uploaded a copy of the title datasets to Kaggle. Moreover, we provide the configurations used to produce the results from the paper. To rerun any of the (title) experiments, do the following: -0. Download the [econbiz.csv](www.kaggle.com) and [pubmed.csv](www.kaggle.com) file, respectively, and copy them to the folder *Resources*. +0. Download the [econbiz.csv and pubmed.csv](https://www.kaggle.com/hsrobo/titlebased-semantic-subject-indexing) files, respectively, and copy them to the folder *Resources*. 2. Open the .cfg file of the respective method that you want to run (MLP, BaseMLP, CNN, or LSTM) from the *Experiments* folder. Copy the command in the third (if you want to evaluate on a single fold) or fifth (if you want to do a full 10-fold-cross-validation) line. 4. In the command, adjust the parameter for the option --tf-model-path parameter (specifies where to save the weights of the models, which can be gigabytes, so make sure you have enough disk space), and the --pretrained_embeddings parameter to the location of the GloVe model in your environment. 5. *cd* to the folder *Code/lucid_ml* and run the command. From d748a5cede9c5b35fa9dbacb675780737375f1e9 Mon Sep 17 00:00:00 2001 From: Florian Mai <florian.ren.mai@googlemail.com> Date: Tue, 3 Apr 2018 09:59:28 +0200 Subject: [PATCH 112/116] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ac3ca1b..24e0ea8 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ pip install -r requirements.txt In order to enhance the reproducability of our study, we uploaded a copy of the title datasets to Kaggle. Moreover, we provide the configurations used to produce the results from the paper. To rerun any of the (title) experiments, do the following: -0. Download the [econbiz.csv and pubmed.csv](https://www.kaggle.com/hsrobo/titlebased-semantic-subject-indexing) files, respectively, and copy them to the folder *Resources*. +1. Download the [econbiz.csv and pubmed.csv](https://www.kaggle.com/hsrobo/titlebased-semantic-subject-indexing) files, respectively, and copy them to the folder *Resources*. 2. Open the .cfg file of the respective method that you want to run (MLP, BaseMLP, CNN, or LSTM) from the *Experiments* folder. Copy the command in the third (if you want to evaluate on a single fold) or fifth (if you want to do a full 10-fold-cross-validation) line. 4. In the command, adjust the parameter for the option --tf-model-path parameter (specifies where to save the weights of the models, which can be gigabytes, so make sure you have enough disk space), and the --pretrained_embeddings parameter to the location of the GloVe model in your environment. 5. *cd* to the folder *Code/lucid_ml* and run the command. 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Mon Sep 17 00:00:00 2001 From: Lukas Galke <git@lpag.de> Date: Sat, 24 Aug 2019 02:06:54 +0200 Subject: [PATCH 114/116] Actually merge READMEs --- README.md | 68 ++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 65 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 24e0ea8..59d8275 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,52 @@ -# Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text +# Quadflor + +![Multi-label classification pipeline](Documentation/graphics/pipelineExtended.png) + +## Multi-label classification + +The multi-label classification task consists of assigning a set of class labels +to a sample of subjects. In the context of scientific or news documents, the +task is equivalent to subject indexing. Using machine learning, the goal is to +generalize from a training set of labeled documents to unlabeled documents. + +## A text classification pipeline + +[Quadflor](https://github.com/quadflor/Quadflor) is a text-processing pipeline +for multi-label classification of documents and its evaluation. Given +a domain-specific thesaurus with descriptor labels, the different algorithms +learn how to assign these labels to documents from a training set. The +framework supports opportunities to conduct concept extraction, synonym set +resolution, spreading activation including hierarchical re-weighting. The +further processing of these features is performed by a classifier. As built-in +classifier options, Quadflor provides: + +- Naive Bayes (two variants) +- Logistic Regression +- Linear Support Vector Machine +- K-Nearest Neighbors (two multi-label adaption variants, as well as Rocchio), +- Stochastic Gradient Descent +- Stacked Decision Tree Classifier +- Learning2Rank +- Multilayer Perceptron (MLP) + +While the text-processing pipeline is designed for automatic +evaluation of novel classification strategies, it can also be employed in +a practical setting using all known documents as training data and classifying +new, unknown documents. + +## Experimental results + +In our [experiments](http://arxiv.org/abs/1705.05311), we compared several configurations of +classification techniques using either the full-text or only the titles of the +documents as input. We found that stochastic gradient descent (SGD) optimizing +logistic loss yields reasonably good classification results with respect to +the training time. SGD attained .485 sample-averaged F1 score on the full-text, .429 on the titles +of our economics data set. When more training time can be afforded, decision +trees on top of the SGD classifier could even raise the performance: .498 on +full-text, .451 on titles. Finally, the Multilayer Perceptron requires the +most training time and leads to the best results: .519 on full-text, .472 on +titles. -This repository contains the code for the JCDL paper [Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text](https://arxiv.org/abs/1801.06717). It is based on and extents the multi-label classification framework [Quadflor](https://github.com/quadflor/Quadflor). ## Installation @@ -17,7 +63,23 @@ cd Code pip install -r requirements.txt ``` -## Replicating the results +## Example Usage + +An example call with tf-idf features and stochastic gradient descent classifier: + +```sh +cd Code/lucid_ml +python3 run.py -tf sgd -k Code/lucid_ml/file_paths.json -K example-titles --interactive +``` + +where `file_paths.json` should contain the key given by `-K` specifying the +paths to data (`X`), the gold standard (`y`), and the thesaurus (`thes`). + +## Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text + +This repository has merged the code for the JCDL paper [Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text](https://arxiv.org/abs/1801.06717) from Florian Mai's [fork](https://github.com/florianmai/Quadflor). + +## Replicating the results of the JCDL paper In order to enhance the reproducability of our study, we uploaded a copy of the title datasets to Kaggle. Moreover, we provide the configurations used to produce the results from the paper. From 2e92f9a0069066b568e75ffa01ac947c313c6003 Mon Sep 17 00:00:00 2001 From: Lukas Galke <git@lpag.de> Date: Sat, 24 Aug 2019 02:15:51 +0200 Subject: [PATCH 115/116] Add hint how to get GloVe vectors --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 59d8275..eaa7661 100644 --- a/README.md +++ b/README.md @@ -86,5 +86,5 @@ In order to enhance the reproducability of our study, we uploaded a copy of the To rerun any of the (title) experiments, do the following: 1. Download the [econbiz.csv and pubmed.csv](https://www.kaggle.com/hsrobo/titlebased-semantic-subject-indexing) files, respectively, and copy them to the folder *Resources*. 2. Open the .cfg file of the respective method that you want to run (MLP, BaseMLP, CNN, or LSTM) from the *Experiments* folder. Copy the command in the third (if you want to evaluate on a single fold) or fifth (if you want to do a full 10-fold-cross-validation) line. -4. In the command, adjust the parameter for the option --tf-model-path parameter (specifies where to save the weights of the models, which can be gigabytes, so make sure you have enough disk space), and the --pretrained_embeddings parameter to the location of the GloVe model in your environment. +4. In the command, adjust the parameter for the option --tf-model-path parameter (specifies where to save the weights of the models, which can be gigabytes, so make sure you have enough disk space), and the --pretrained_embeddings parameter to the location of the GloVe word vectors file, which you need to download [here](https://nlp.stanford.edu/projects/glove/). 5. *cd* to the folder *Code/lucid_ml* and run the command. From d052329a98757491e87257868575572bf303a81f Mon Sep 17 00:00:00 2001 From: Lukas Galke <git@lpag.de> Date: Sat, 24 Aug 2019 02:16:30 +0200 Subject: [PATCH 116/116] Remove word vector file --- Code/lucid_ml/utils/glove.6B.50d.txt | 50001 ------------------------- 1 file changed, 50001 deletions(-) delete mode 100644 Code/lucid_ml/utils/glove.6B.50d.txt diff --git a/Code/lucid_ml/utils/glove.6B.50d.txt b/Code/lucid_ml/utils/glove.6B.50d.txt deleted file mode 100644 index e8b0b0b..0000000 --- a/Code/lucid_ml/utils/glove.6B.50d.txt +++ /dev/null @@ -1,50001 +0,0 @@ -50000 50 -the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 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0.57974 0.036345 0.0090949 0.25772 3.4402 0.20732 -0.52028 0.026453 0.17895 -0.017802 0.36605 0.34539 0.41357 -0.2497 -0.49227 0.17745 -0.43764 -0.3484 -0.057061 -0.039578 -0.13517 -0.4258 0.13681 -0.77731 -new 0.19511 0.50739 0.0014709 0.041914 -0.16759 0.037517 -1.397 -0.92398 -0.24296 -0.15171 -0.47829 0.054612 -0.24986 0.38398 0.016182 0.34938 -0.22627 0.086618 -0.41001 -0.18139 0.75607 -0.0262 -0.69557 0.10874 -0.47539 -1.8095 -0.1694 -0.059863 -0.16806 -0.094546 3.661 0.041462 -0.29161 -0.69772 0.30805 -0.28457 0.13217 -0.007643 -0.09239 -0.49237 -0.27055 0.060425 0.095107 -0.23679 -0.086108 1.0243 -0.22779 0.030488 -0.14272 0.45411 -been 0.92884 -0.72457 0.068095 -0.3816 -0.038686 0.22314 -1.1041 0.0084314 -0.26638 -0.057147 0.33383 -0.02368 -0.7689 -0.17933 0.84499 0.28781 -0.12754 0.11154 -0.34022 -0.18687 -0.28446 0.32557 0.87015 -0.21355 -0.094175 -2.0216 -0.2176 0.45054 -0.14068 0.080753 3.7849 -0.4107 0.33195 -0.87998 0.17309 0.14065 0.22707 0.485 -0.51256 -0.021742 -0.69401 0.145 0.0082681 0.38385 0.16011 -0.35487 -1.1284 0.047085 -0.32297 -0.64192 -also 0.352 0.25323 -0.097659 0.26108 0.12976 0.33684 -0.73076 -0.42641 -0.22795 -0.083619 0.52963 0.34644 -0.32824 -0.28667 0.24876 0.22053 0.019356 -0.015447 -0.18319 -0.29729 0.11739 -0.071214 0.41086 0.013912 -0.17424 -1.5839 -0.051961 -0.18115 -0.76375 -0.17817 3.749 -0.045559 0.10721 -0.51313 0.25279 -0.051714 0.31911 0.28 -0.19937 0.17819 0.018623 0.47641 -0.15655 -0.38287 0.26989 -0.011186 -0.7244 0.036514 -0.011489 -0.025882 -we 0.57387 -0.32729 0.070521 -0.4198 0.862 -0.80001 -0.40604 0.15312 -0.29788 -0.1105 -0.097119 0.59642 -0.99814 -0.28148 1.0152 0.87544 1.0282 -0.05036 0.24194 -1.1426 -0.50601 0.64976 0.74833 0.020473 0.9595 -1.9204 -0.80656 0.29247 1.0009 -0.98565 4.0094 1.0407 -0.82849 -0.4847 -0.36146 -0.39552 0.27891 0.15312 0.15848 0.018686 -0.50905 -0.22916 0.1868 0.44946 0.10229 0.21882 -0.30608 0.48759 -0.18439 0.69939 -would 0.7619 -0.29773 0.51396 -0.13303 0.24156 0.066799 -0.54084 0.2071 -0.28225 -0.11638 0.21666 0.54908 -0.36744 -0.10543 0.81567 1.1743 0.56055 -0.3345 0.099767 -0.87465 0.12229 -0.18532 0.086783 -0.36343 0.008002 -2.2268 -0.20079 -0.10313 0.24318 -0.39819 3.7136 0.59088 -1.1013 -0.25292 0.0057067 -0.60475 0.35965 -0.059581 -0.029059 -0.3989 -0.52631 0.12436 0.13609 0.12699 -0.23032 -0.044567 -0.6545 0.43088 -0.22768 0.4026 -two 0.58289 0.36258 0.34065 0.36416 0.34337 0.79387 -0.9362 0.11432 -0.63005 -0.55524 -0.28706 -0.47143 -0.75673 0.63868 0.22479 -0.64652 -0.074314 -0.34903 -0.97285 -0.53981 0.015171 0.24479 0.62661 0.070447 -0.51629 -1.2004 0.3122 -0.44053 -0.29869 -0.56326 4.022 0.38463 -0.028468 0.068716 1.0746 0.48309 0.2475 0.22802 -0.35743 0.40392 -0.54738 0.15244 0.41 0.15702 0.0077935 -0.015106 -0.28653 -0.16158 -0.35169 -0.82555 -more 0.87943 -0.11176 0.4338 -0.42919 0.41989 0.2183 -0.3674 -0.60889 -0.41072 0.4899 -0.4006 -0.50159 0.24187 -0.1564 0.67703 -0.021355 0.33676 0.35209 -0.24232 -1.0745 -0.13775 0.29949 0.44603 -0.14464 0.16625 -1.3699 -0.38233 -0.011387 0.38127 0.038097 4.3657 0.44172 0.34043 -0.35538 0.30073 -0.09223 -0.33221 0.37709 -0.29665 -0.30311 -0.49652 0.34285 0.77089 0.60848 0.15698 0.029356 -0.42687 0.37183 -0.71368 0.30175 -' -0.039369 1.2036 0.35401 -0.55999 -0.52078 -0.66988 -0.75417 -0.6534 -0.23246 0.58686 -0.40797 1.2057 -1.11 0.51235 0.1246 0.05306 0.61041 -1.1295 -0.11834 0.26311 -0.72112 -0.079739 0.75497 -0.023356 -0.56079 -2.1037 -1.8793 -0.179 -0.14498 -0.63742 3.181 0.93412 -0.6183 0.58116 0.58956 -0.19806 0.42181 -0.85674 0.33207 0.020538 -0.60141 0.50403 -0.083316 0.20239 0.443 -0.060769 -0.42807 -0.084135 0.49164 0.085654 -first -0.14168 0.41108 -0.31227 0.16633 0.26124 0.45708 -1.2001 0.014923 -0.22779 -0.16937 0.34633 -0.12419 -0.65711 0.29226 0.62407 -0.57916 -0.33947 -0.22046 -1.4832 0.28958 0.081396 -0.21696 0.0056613 -0.054199 0.098504 -1.5874 -0.22867 -0.62957 -0.39542 -0.080841 3.5949 -0.16872 -0.39024 0.026912 0.52646 -0.022844 0.63289 0.62702 -0.22171 -0.45045 -0.14998 -0.27723 -0.46658 -0.44268 -0.43691 0.38455 0.1369 -0.25424 0.017821 -0.1489 -about 0.89466 0.36604 0.37588 -0.41818 0.58462 0.18594 -0.41907 -0.46621 -0.54903 0.02477 -0.90816 -0.48271 -0.050742 -0.74039 1.4377 -0.01974 -0.2384 0.43154 -0.6612 -0.41275 0.25475 0.93498 0.81404 -0.17296 0.61296 -1.8475 -0.27616 0.27701 0.42347 -0.11599 3.6243 0.12306 -0.023526 -0.24843 -0.22376 -0.53941 -0.62444 -0.27711 0.49406 0.020234 -0.2346 0.44512 0.53397 0.66654 -0.093662 -0.035203 -0.064194 0.55998 -0.66593 0.12177 -up 0.032286 -0.27071 0.68108 -0.27942 0.5797 -0.0081097 -0.82792 -0.53342 -0.47851 -0.068256 -0.46964 -0.31717 -0.49372 0.09808 0.49961 0.27305 0.099922 -0.16148 -0.69952 -0.70435 0.59084 0.62031 0.30467 -0.41578 -0.0222 -1.6312 0.54676 0.25754 0.44541 -0.72799 3.9129 0.80075 -0.18839 0.42435 0.039207 -0.093939 -0.39516 0.20976 0.59488 -0.3907 -0.31555 0.24074 0.41694 0.10415 -0.044305 -0.09516 0.25464 -0.56699 0.033216 -0.58123 -when 0.27062 -0.36596 0.097193 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-0.099214 0.20658 -0.73315 0.86418 -0.26917 -0.93792 1.919 -0.17473 -0.61905 -0.5035 -0.9341 -1.6755 -0.79399 -0.32003 -0.067678 -0.12344 3.2909 -0.43151 0.3879 0.38733 -0.018419 -0.98191 -0.53189 0.39866 0.89452 -0.40963 0.58922 -0.52377 0.27893 -0.25766 -0.30457 -0.066305 -1.1221 0.21582 -0.15586 0.64018 -group 0.74048 -0.1201 0.039916 0.77326 0.80822 0.32251 -0.77215 -0.35964 0.052412 0.088437 0.81699 -0.43037 -0.83637 0.44307 -0.76235 -0.46153 0.51897 0.010496 -0.66992 -0.15137 0.34948 0.45595 0.18992 0.54493 -1.4771 -1.1608 -0.74422 -0.86467 -1.278 -0.064544 3.6217 0.056087 -0.29725 -0.081222 -0.32781 -1.0023 -0.47127 -0.8349 0.027287 -0.096889 0.21902 -0.74387 0.12332 -0.40643 1.0687 -0.19225 -1.1237 0.076487 -0.28012 -0.051703 -any 0.51292 0.09032 0.023552 0.21438 0.70226 0.5623 0.11839 0.25641 -0.039706 0.095389 -0.015477 0.37808 -0.1616 -0.18322 0.80726 0.521 0.40167 -0.51684 -0.056347 -0.95212 0.042801 -0.082442 0.23867 -0.10777 0.12598 -2.033 -0.48642 -0.018917 1.0034 -0.16553 3.8562 0.10825 -0.81231 -0.82064 0.098151 -0.025282 0.27985 -0.28044 -0.40594 -0.14185 0.0028418 -0.046259 0.093178 0.9383 -0.039413 -0.35659 -0.33987 1.1585 0.29796 0.075048 -through 0.64925 0.068384 0.031703 -0.51479 -0.52809 -0.068008 -0.80786 -0.080712 0.52543 -0.020451 -0.26222 -0.077934 -0.54208 -0.0044965 -0.18831 -0.058391 -0.10829 -0.32967 -0.37944 -0.30563 0.74476 0.18284 0.31539 0.18516 0.32603 -1.4627 0.29471 0.2981 0.3911 -0.71861 3.9504 0.32215 -0.11705 -0.57798 -0.35637 0.29018 -0.41629 0.26993 0.23642 0.25035 -0.074786 -0.1193 -0.12565 0.25444 -0.60505 -0.12684 0.70263 -0.81355 -0.29753 -0.80957 -china -0.22427 0.27427 0.054742 1.4692 0.061821 -0.51894 0.45027 -0.32164 0.57876 -0.049142 0.52523 -0.18784 0.52539 -0.058431 0.19741 0.30754 -0.45412 0.38268 0.33441 0.42801 0.98406 -0.7637 -0.066755 -1.0027 1.1942 -2.3916 0.24326 -0.40705 -0.63413 -0.20832 3.8851 0.75046 0.14857 0.24485 -1.0143 -0.76356 -0.63824 0.73037 -1.2025 0.18932 -1.2139 -0.55377 1.3033 -0.82461 0.9965 0.15117 -0.65753 0.28569 0.45374 -0.85646 -four 0.33375 0.44809 0.54759 0.21497 0.32637 0.70326 -0.95872 0.057798 -0.43454 -0.55322 -0.40482 -0.65793 -0.75076 0.58097 0.51968 -0.86607 -0.10026 -0.11609 -1.2668 -0.46131 -0.10421 0.13894 0.70448 -0.0040651 -0.56479 -0.99797 0.30126 -0.53745 -0.42073 -0.49521 3.9051 0.43253 0.017059 0.31489 1.2546 0.52463 0.46232 0.29622 -0.23879 0.12913 -0.75519 -0.065684 0.52592 0.20047 -0.15982 0.18293 -0.29966 -0.1926 -0.30174 -0.80437 -being 0.59049 -0.66076 -0.02551 -0.66217 0.3834 0.31517 -0.82654 0.14879 -0.36427 0.015319 0.51745 0.2739 -0.42352 -0.1069 0.65642 0.13723 -0.011125 0.38913 -0.54748 -0.15923 -0.26029 0.37735 0.83593 0.16307 0.013573 -2.0174 -0.15106 0.49917 0.1841 -0.17101 3.5601 -0.35118 0.24638 -0.94568 0.22149 0.22784 0.56643 0.41457 -0.58431 -0.39573 -0.26413 -0.18254 0.00017507 0.47701 -0.15756 -0.43371 -1.0655 -0.41736 0.086398 -0.38974 -down -0.1981 -0.70847 0.85857 -0.48108 0.51562 -0.28924 -0.64311 -0.41966 -0.68718 -0.12307 -0.44594 -0.35226 -0.84826 0.12962 0.20005 0.48916 -0.20257 -0.53341 -0.96196 -0.67681 0.69808 0.50599 0.24012 -0.72813 0.11396 -1.8256 0.8098 0.65007 0.73111 -0.50325 3.5865 0.30906 -0.14279 0.6753 -0.074059 -0.45274 -0.28001 -0.20383 0.61044 -0.24875 -0.47409 0.25916 0.41522 0.15245 0.093191 -0.091906 0.40082 -0.90268 0.30191 -0.89862 -war 0.36544 -0.15746 -0.23966 -1.0307 -0.070691 0.21397 -0.041914 0.28451 -0.60803 -1.1451 -0.32878 -0.20474 -0.81884 -0.50994 0.64189 -0.54164 0.019143 0.18778 -0.66691 0.93691 0.03469 -0.04457 -0.014529 -0.76871 -0.20773 -2.551 -0.96284 -0.61126 1.2183 1.1249 3.0499 -0.38817 -1.0091 -0.47156 -0.56735 0.53067 -0.61745 -0.61581 -1.1875 -0.21176 -0.50487 -0.24842 0.53851 -0.98083 0.089771 0.24673 -0.33966 0.011441 -1.4997 -0.39907 -back 0.0046218 -0.25678 0.3532 -0.82697 0.099033 -0.27524 -1.2297 0.37345 -0.44635 -0.36967 -0.12421 0.20837 -1.1801 0.045587 0.47457 0.40557 0.046003 -0.62809 -0.70816 -0.42283 -0.24011 0.44871 0.14759 -0.17298 0.31948 -2.0483 0.43036 0.44567 0.52804 -0.72942 3.5192 0.72008 -0.19861 0.42855 0.029422 0.15914 -0.16062 0.3479 0.35462 -0.53138 -0.28746 0.35596 -0.083996 -0.0064132 -0.32994 0.055304 0.50614 -0.80837 -0.33017 -0.62638 -off 0.45341 -0.71044 0.25826 -0.015757 0.14649 0.031418 -1.0812 0.31886 0.055295 -0.12677 -0.45668 0.030006 -0.81975 0.55073 0.15524 -0.018467 0.074422 -0.56719 -1.5538 -0.60283 0.16099 0.45487 0.17386 -0.24204 0.38416 -1.9726 1.013 0.80808 0.88332 -0.9352 3.5564 0.5633 0.036648 0.94056 0.045721 0.013343 -0.27625 0.14873 0.37149 -0.15766 -0.33456 0.71518 0.24446 0.087958 -0.11456 0.13934 0.33379 -0.84455 0.24269 -0.66447 -south -0.039674 0.47097 -0.31102 0.38088 -0.59254 -0.38231 -0.68876 0.15252 0.46303 -0.9099 -0.38665 -0.82695 0.035915 -0.93486 -0.82315 0.37535 0.93198 0.19098 -1.01 0.59531 -0.1818 0.34694 0.096477 0.48152 0.19392 -1.9725 0.77759 0.16259 -0.68996 -0.12239 3.5077 -0.23937 0.12591 0.22983 0.5462 -0.74276 -0.30525 -0.19996 -0.77648 -0.13621 -0.54883 0.068693 0.88314 -1.0529 0.18614 0.88575 -0.43661 -0.49549 -0.10562 -0.77821 -american -1.0988 0.48544 -0.5004 0.1491 0.41042 0.32605 -1.4592 -0.52025 -0.088054 -0.2384 0.43632 -0.14949 0.12491 0.71661 0.037183 -0.60692 0.034298 0.017432 -0.87692 -0.21223 0.10612 0.61057 -0.44873 -0.03961 -0.23466 -2.0954 -0.85982 -0.97872 0.068549 0.2635 3.0317 -0.38539 -0.17444 -0.47553 -0.083736 -0.65646 -0.768 -0.30997 -0.44042 -0.34611 -0.15336 0.0090824 1.2306 -0.35248 0.87702 0.31172 -0.54675 -0.10366 -0.86998 0.42675 -minister 0.039931 0.50725 -0.18625 0.32268 0.65427 0.20286 -0.4966 0.13383 -1.7112 -1.0943 0.10988 0.74713 -0.32761 0.66333 0.60602 0.36298 0.54733 -0.80094 1.2633 0.81898 0.49794 0.58007 -0.24326 -0.72633 0.82971 -1.8744 1.2554 -0.24206 -1.1283 1.8337 2.8967 0.067548 -0.97245 0.14373 -0.18087 0.58634 0.52765 0.46389 -0.031852 0.90933 -0.39749 1.6654 -0.28879 -1.9615 0.89356 0.15835 -2.1877 0.93192 1.9066 -0.1589 -police 0.49725 -1.1949 0.37137 -0.081662 0.69114 -0.69982 -0.25723 0.5943 0.059978 -1.499 -0.07122 -1.0053 -0.73845 -0.40988 0.43074 -0.46757 -0.36498 0.29674 -0.62775 -0.41573 0.28614 1.1718 -0.21516 0.62029 -0.85242 -2.4672 0.14414 0.066415 -0.37916 -0.65373 2.7482 -0.28856 -0.45409 -1.354 0.58534 1.0112 0.67715 -1.1708 0.36475 1.1886 -0.28727 1.2292 0.58489 -0.20625 0.90859 -0.88349 -0.85085 0.12378 0.85397 -0.65035 -well 0.27691 0.28745 -0.29935 -0.19964 0.12956 0.15555 -0.64522 -0.3409 -0.11833 0.15798 0.13969 0.24872 -0.15901 -0.033439 0.11895 0.076535 0.45263 0.26494 -0.19157 -0.56768 0.029286 0.21745 0.43406 0.14981 0.075774 -1.4453 -0.58394 -0.046063 0.066214 -0.26417 3.965 0.25196 0.24855 -0.50524 0.25806 0.28683 -0.17994 0.62885 -0.1204 -0.042143 -0.044911 0.18561 0.16266 -0.0026127 0.13083 0.20179 -0.29667 -0.09482 -0.2125 0.022074 -including 0.48518 0.56214 0.064657 0.44652 -0.29881 0.91376 -0.84552 -0.93462 0.093077 0.05312 -0.25653 -0.16388 -0.084874 -0.084588 0.28105 -0.55097 -0.022583 0.078236 -0.32487 -0.28224 0.51683 0.22509 0.20189 -0.21816 -0.81741 -1.0254 -0.27715 -0.80942 -0.61824 -0.22689 3.47 0.17391 0.13567 -0.41534 0.61925 0.50792 -0.08481 -0.10788 -0.23281 0.50943 -0.37383 0.59976 0.55221 -0.078815 0.47338 0.070097 -0.8109 0.0085293 -0.43153 -0.40666 -team -0.62801 0.12254 -0.3914 0.87937 0.28572 -0.41953 -1.4265 0.80463 -0.27045 -0.82499 1.0277 0.18546 -1.7605 0.18552 0.56819 -0.38555 0.61609 0.51209 -1.5153 -0.45689 -1.1929 0.33886 0.18038 0.10788 -0.35567 -1.5701 -0.02989 -0.38742 -0.60838 -0.59189 2.9911 1.2022 -0.52598 -0.76941 0.63006 0.63828 0.30773 1.0123 0.0050781 -1.0326 -0.29736 -0.77504 -0.27015 -0.18161 0.04211 0.32169 0.018298 0.85202 0.038442 -0.050767 -international -0.1666 0.68994 -0.76138 0.55857 -0.28302 -1.0075 -0.35157 -0.46751 0.99938 0.21826 0.87406 0.22722 -1.0739 0.32807 0.14755 -0.10786 0.57761 0.065902 -0.73381 -0.29534 1.1916 0.70041 -0.77054 0.04179 -0.84269 -1.6856 0.33451 -0.6543 -1.2509 0.34069 3.3936 0.81089 -0.48503 -0.85079 -0.49627 -0.45548 -0.17286 -0.10367 -0.28043 0.035118 0.09691 0.0286 1.0817 -0.47067 -0.0069481 0.48139 -0.34924 0.91937 -0.082162 -0.46327 -week 0.25009 -0.033956 0.094114 0.32336 -0.01654 -0.63211 -1.2778 0.32265 -0.14109 -0.29001 -0.73881 -1.0599 -0.69152 0.066963 1.4545 0.24474 -0.7986 -0.84672 -0.80999 -0.10354 0.59366 0.47797 0.33725 -0.42843 -0.34131 -1.8441 0.30726 0.29596 -0.40516 0.34157 3.4163 0.4477 -0.43271 0.12544 0.055593 -0.57397 0.18095 0.092258 0.072602 -0.48368 -0.71812 0.39523 -0.12913 -0.2552 0.41018 0.1954 -0.12005 0.8398 -0.29115 0.078721 -officials 0.99818 -0.44131 0.39641 0.36621 -0.23042 -0.84895 -0.82396 0.63432 0.078578 -1.0304 -0.11953 -0.44399 -0.19639 -0.080367 0.5077 0.38944 -0.32942 -0.12153 0.083004 -0.62174 0.16542 0.3213 0.31803 -0.46028 -0.4711 -2.6599 0.14648 0.33607 -0.83768 -0.20686 3.1996 0.23469 -0.65633 -1.075 0.047687 -0.20781 -0.0088134 -0.47237 -0.07229 0.4267 -0.81364 0.49956 0.70998 0.2002 0.68304 -0.30359 -1.1561 1.613 -0.043788 -0.46821 -still 0.47689 -0.076447 0.36768 -0.3947 0.47169 -0.038062 -0.78328 0.14814 -0.30044 -0.072707 -0.030916 -0.16829 -0.53569 -0.16432 0.78732 0.52064 0.15539 -0.0074076 -0.0546 -0.6647 -0.51966 0.40417 0.24546 -0.0038311 0.35098 -1.8132 -0.51917 0.50432 0.49551 -0.085036 3.5993 0.29352 0.26488 -0.36079 -0.013431 -0.11533 -0.1946 0.31915 0.014437 -0.42448 -0.56024 0.023142 0.31161 0.44714 0.039094 0.23879 -0.45699 -0.10113 -0.38907 -0.37151 -both 0.25707 0.25305 -0.082529 0.12036 0.19351 0.66386 -0.45209 -0.084674 -0.57591 -0.0055412 0.35637 0.1987 -0.68307 -0.15141 -0.093202 0.11426 0.27492 -0.23495 -0.21676 -0.61231 -0.1915 0.19664 0.51456 0.16767 0.033627 -1.4032 -0.077227 -0.32463 -0.32382 -0.23254 3.6787 0.34334 0.16075 -0.75931 0.36933 -0.10338 -0.06278 0.47076 -0.64781 -0.055166 -0.096726 0.11986 0.14052 0.018292 -0.05436 0.12612 -0.40612 -0.095894 -0.35745 -0.17943 -even 0.38336 -0.095871 0.12229 -0.51625 0.3491 0.1705 -0.55374 -0.0017357 -0.47808 0.43859 -0.25184 0.16347 -0.22387 -0.017963 0.80635 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0.099316 0.13349 -0.43219 -0.56241 -0.37744 -0.31458 -1.3884 -0.87126 0.055197 -0.27641 -0.56944 0.073814 0.075382 -decision 0.020637 -0.59704 -0.42771 0.45118 0.37551 -0.090939 0.18943 0.82289 -0.18554 -0.11665 -0.028869 0.082758 -0.71106 -0.082236 0.78368 0.64198 0.20384 -1.0338 0.10133 -0.62621 0.062248 -0.022195 -0.36175 -0.5099 0.0037395 -2.3864 0.27427 0.027299 -0.34705 0.052486 2.914 -0.099187 -1.1691 -0.44007 -0.34964 -1.1051 0.91434 -0.072429 -0.40384 -0.57132 -0.31572 0.07733 -0.17125 0.14912 -0.36032 0.50577 -0.69998 1.051 0.093151 0.61208 -stock -0.0743 -0.77841 0.49341 0.73968 0.7554 -0.78968 -0.63113 -0.54916 -0.43567 0.68255 0.074013 0.36359 -0.60005 -0.34957 0.42048 0.73399 -1.111 -0.39159 -0.97131 -0.85833 2.0588 -0.39448 0.11897 -0.23794 -0.97022 -1.0812 -0.16123 0.18695 0.36667 0.30179 3.2035 0.037224 1.132 0.67364 0.45764 -1.6357 -1.2271 -0.29407 0.6426 -0.74074 -0.43575 -0.3363 0.45234 0.57153 0.0029191 0.10863 0.31306 0.49593 0.82498 -0.16104 -america -0.13124 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1.1865 -0.10865 1.4757 0.73861 -0.059645 0.99569 0.24435 -2.0641 -0.58036 1.4317 0.26527 -0.19271 -1.3974 -1.1261 0.22634 -1.9279 0.25428 -0.47793 -0.36595 0.12171 -1.5167 0.98992 0.22429 0.48438 0.18187 2.7346 0.79748 0.99215 -0.38546 0.24519 1.4733 1.4198 0.5224 1.0245 -0.77324 -0.207 0.44724 -0.36914 0.53611 0.42736 -0.11228 0.66118 0.41869 0.20125 -0.25606 -type 0.4545 0.44867 0.25098 0.19588 0.15144 0.84342 0.71705 -0.93999 -0.17253 -0.11095 0.53159 0.63268 0.21535 0.28225 -0.26312 0.19534 -0.57367 0.78071 -0.73573 -0.5267 -0.68811 -1.0655 0.19512 0.3593 0.050894 -1.2452 -0.95129 0.0018235 0.94321 0.086563 3.0592 -0.8666 -0.0656 -0.24285 0.65587 0.14984 0.88066 0.067367 -0.32547 -0.057152 0.48098 -0.56649 0.51436 0.80887 0.7096 0.33316 0.48097 -0.018729 0.54854 0.77098 -planning 1.0782 -0.070636 -0.10853 0.18886 0.04225 -0.54108 -0.43116 -0.57887 0.14898 -0.2882 -0.317 -0.049955 -0.40001 -0.02331 0.1222 0.04445 0.30555 0.27669 0.33215 0.18424 1.1714 0.10704 -0.60995 -0.14559 -0.15045 -1.2497 0.088093 -0.88268 -0.071729 0.2363 2.8883 0.28864 -1.2939 -1.1249 0.049658 0.38681 0.08341 0.22532 -0.10342 0.5568 -0.73785 -0.15226 0.10483 -0.18371 0.29228 -0.57251 -0.28479 0.74043 0.65694 0.9258 -b -0.30116 0.33204 0.15823 0.90937 0.81527 0.57844 0.71019 -1.0302 0.27123 0.79128 0.51991 0.98376 -0.93796 -0.24859 -0.51284 0.19092 0.11299 -0.55316 -1.126 0.26185 -0.42071 0.3091 1.4791 0.92466 -0.54612 -0.18773 0.49344 -0.61355 -0.43817 0.12065 2.787 -0.21129 0.5784 0.7635 1.2451 -0.67338 1.1734 -0.011767 -0.11209 0.1442 0.61184 0.23579 0.056863 -0.13761 -0.21982 1.0443 0.019525 -0.77967 0.98732 0.62457 -environment 0.24989 0.21958 -0.44505 0.58602 -0.010814 -0.39337 -0.12406 -1.1307 0.43009 -0.050078 0.72717 0.37415 0.51648 0.55305 -0.014879 0.8714 1.1501 0.30417 0.62127 -0.42837 -0.021624 0.58691 -0.36424 -0.20318 0.87405 -0.33359 0.3524 0.086728 0.18502 1.364 3.3352 -0.064015 -0.10601 -1.8814 -0.9336 0.31382 -0.39991 0.45189 0.31445 -0.075881 -0.55053 -0.090056 0.8603 -0.026639 0.2439 0.30208 -0.44115 -0.036279 1.1414 0.7708 -sites 1.1563 0.66751 0.77374 0.73905 -0.28121 -0.76516 -1.4736 -0.64058 1.0652 -0.0099842 -0.4561 -0.69793 0.55471 -0.098908 0.3753 0.47367 0.43184 -0.4382 -0.075352 0.23866 0.50378 -0.45999 0.070698 0.90933 -0.16626 -0.69843 -0.67594 -0.26481 0.58736 -0.68717 2.8757 -0.56654 -0.083249 -1.2134 -0.13512 0.62592 0.29027 0.0086954 -0.97173 0.54144 0.5794 -0.64737 0.5526 1.0629 0.51849 0.10684 -0.51989 0.5414 -0.018431 -0.60995 -protest -0.13504 -0.095845 -0.49184 -0.42418 -0.6669 -0.39775 -0.021353 0.54799 -0.42971 0.13046 -0.44178 -1.4305 0.16694 -0.11624 0.71307 -0.42882 -0.094519 -0.33829 -0.32803 -0.71316 0.88262 0.16781 -0.86799 -0.079242 -0.13241 -1.6707 0.66902 -0.043954 -0.19008 0.44734 2.6339 0.25684 -1.3236 -0.21172 -1.2872 -0.17829 0.76906 -1.2877 -0.73998 0.67301 -0.31387 0.32692 -0.2752 -0.14334 1.1163 -0.27074 -0.95499 -0.0066052 0.031894 -0.96131 -drop -0.24837 -0.463 0.82342 -0.46939 0.36414 0.34379 -0.40435 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-0.062841 -0.060393 -0.50369 -0.063051 0.17516 -0.11931 -0.30113 0.31668 -0.068968 0.52393 -0.046753 -0.22078 -0.33522 -signing -0.074691 0.75841 -0.6599 -0.047426 0.33205 -0.22788 -1.4443 0.22967 -0.60403 0.62891 0.25062 0.52505 -0.68067 -0.12102 1.1318 -0.30721 0.016359 -0.84537 0.11448 0.29631 0.20066 0.10996 -0.39219 -0.64648 -0.59497 -0.90117 1.4919 0.017938 -0.12946 -0.55458 2.4276 0.63999 -0.90877 -0.038221 0.068282 0.049805 0.47024 0.010806 -0.56568 -0.52436 0.058793 -0.36115 -0.61401 -0.49962 -0.49051 -0.29971 -0.22201 1.3345 -0.55423 -0.39475 -tower 1.1474 1.1811 0.74556 -0.059101 0.50499 -0.70449 -0.32136 -0.4539 -0.45763 -0.75341 -0.33511 -0.024975 -0.50192 0.63773 -0.83059 0.83565 -0.24701 0.32421 -1.1103 -0.021335 0.68717 -0.3934 -1.639 -0.50493 -0.16684 -0.67649 -0.31798 0.88503 -0.031552 -0.15608 1.9805 -1.187 0.83342 -0.18369 -0.26691 0.11619 1.1023 -0.35937 0.025015 -0.040615 0.30681 -0.41076 0.084586 0.22475 -0.50955 0.65819 -0.12432 -1.4039 0.00016178 -0.52529 -option 0.53979 -0.10146 0.39904 -0.11172 0.2761 0.35842 -0.48671 -0.1535 -0.24169 0.63167 -0.00045599 0.34756 0.010326 0.092891 0.4244 0.57127 0.21813 0.18189 0.26642 -0.57327 0.03392 -0.34923 -0.57147 0.36076 -0.54705 -1.1507 0.669 -0.11681 0.826 0.0021739 2.7711 0.27237 -1.0201 0.17365 1.0541 -0.31271 0.47299 0.25141 -0.56074 -0.94785 -0.03765 -0.3138 0.11377 0.88362 -0.83095 -0.20903 0.15516 1.0956 0.12524 0.93829 -expert -0.25734 0.24649 -0.42792 0.38292 0.31951 -0.23442 -0.26921 -0.50961 0.97492 -0.54526 -0.010812 0.40735 -0.099967 0.54751 0.14443 -0.15385 0.53509 0.16731 -0.0057508 0.11507 0.25951 0.62819 -0.39374 -0.22706 0.24714 -1.9596 -0.45863 -0.77271 -1.0922 0.28479 1.805 -1.0415 -0.26373 -1.1018 -0.077355 0.35368 -0.32719 0.61167 0.78398 0.2226 0.19461 0.61858 0.81981 0.19764 1.5157 -0.054299 0.25981 1.0405 0.15525 0.70445 -herself 0.31447 0.10151 -0.066704 -0.80305 0.89423 1.3251 -0.19217 0.60825 0.04408 0.19699 -0.01047 0.1115 -0.3566 0.19738 0.51883 0.095536 -0.40567 0.13349 0.65261 -0.079423 0.27023 1.4548 0.25115 0.36705 1.2269 -1.8877 -0.27451 0.25589 0.20663 -0.8249 1.8044 0.18337 -0.08148 -0.5448 -0.43868 0.26839 0.00047471 0.76837 -0.19776 -1.1183 0.24236 -0.086667 -0.10965 -0.44555 0.18206 -0.54624 -0.2093 -1.5462 0.40268 0.11407 -portion 0.53791 0.62463 0.10798 0.2375 0.19825 0.4536 -0.2291 0.33695 0.98573 -0.56517 -0.36199 -0.62971 0.89302 -0.53004 -0.50762 0.37774 0.19577 0.15567 -0.12763 0.1399 0.52947 -0.97837 -0.044011 0.39873 -0.46362 -1.0677 -0.37463 0.78482 0.40991 -0.55551 2.8812 -0.54851 0.5865 0.14884 0.11156 -0.10421 -0.17983 0.34103 0.06166 -0.25501 -0.028762 -0.19905 0.021794 0.35428 -1.6193 -0.029217 0.099106 -0.044154 -0.40316 -0.21815 -deals 0.68632 -0.047277 -0.098332 0.61968 0.046561 0.30835 -0.75481 -0.37681 -0.16429 0.83188 -0.69573 0.55616 -0.6064 0.26784 0.73982 -0.11517 0.030315 -0.26372 0.50666 -0.27409 1.3951 0.022068 0.025351 -0.20077 -0.82817 -0.92855 0.1975 -0.04447 0.52621 -0.22807 2.7124 0.17178 0.08522 0.23614 0.18173 -0.28755 -0.88178 -0.025985 -0.77351 -0.36476 -0.43605 -0.30667 0.57249 0.625 -0.45072 -0.31964 -0.38504 1.4436 -0.43927 -0.15362 -pre 0.26318 0.055605 -0.25071 -0.004784 -0.20655 -0.2168 -0.33329 -0.30542 0.12155 0.39984 -0.40387 -0.82618 -0.017275 0.077742 0.75285 0.32591 -0.73418 -0.6304 -0.5395 0.17132 0.33444 0.080619 -0.055464 -0.40968 -0.57142 -0.86444 -0.6135 -0.13361 0.13163 0.52885 2.5179 0.62638 -0.74453 0.20142 0.1932 -0.13021 0.42708 0.25843 -0.36733 -0.63503 -0.36465 -0.16329 -0.085794 0.47481 0.38848 0.029355 0.28723 0.67125 -0.10134 0.045852 -competitive -0.81014 -0.28234 -0.28727 0.6359 -0.27351 -0.071608 -0.14112 0.076825 0.26304 0.41609 0.46557 -0.12236 -0.60515 0.1737 -0.047067 0.15722 0.61511 0.81469 -0.25682 -1.2257 0.20692 -0.40369 -0.41984 0.46277 -0.063259 -1.0457 0.18055 -0.30216 0.4177 0.66108 2.887 1.1282 0.51252 -0.57985 0.16743 -0.24078 -0.63805 1.2851 -0.74262 -1.3115 -0.49869 -0.76 0.16423 0.8013 0.019521 -0.1467 -0.0069646 0.38835 0.28754 0.96041 -spirit -0.017501 0.79698 -1.3675 -0.50521 1.197 -0.12511 -0.13959 -0.28019 0.42817 0.98117 0.48314 0.79808 -0.083251 0.065461 -0.68807 -0.22736 0.2863 0.55286 -0.52502 -0.17508 -0.30372 1.1152 -0.24035 -0.46058 0.7675 -1.1378 -0.971 -0.15074 0.68565 -0.045888 2.1921 0.76795 -1.0418 -0.47572 -0.4712 -0.63274 -0.45774 -0.60072 -0.58997 -0.22618 -0.034206 -1.1107 -0.4165 -0.14773 -0.03899 0.67673 -0.43615 -0.37747 -0.22257 -0.35182 -laid 0.50701 0.34389 -0.054648 -0.77665 0.29928 0.22264 -0.7038 -0.24621 -0.56249 -1.194 -0.45069 0.050942 -0.32712 -0.22572 -0.11084 0.020381 -0.092075 0.10447 0.16731 -0.55509 0.46039 0.021473 0.029771 -1.05 -0.050936 -0.83648 0.073308 0.5406 0.20849 -0.063328 2.256 -0.44038 -0.57223 -0.05401 -0.17707 0.19976 0.33729 0.42469 -0.060859 0.26426 -0.38887 -0.83171 0.27091 -0.00839 0.081215 -0.21307 -0.51601 -0.41557 -0.15422 -0.73269 -winners -0.67242 1.0109 0.19408 0.86357 -0.24212 0.23809 -0.27161 0.80439 -0.38743 0.41399 -0.38097 -0.33101 -0.71998 -0.047742 0.80557 -0.93232 0.90865 -0.49274 -1.2216 -0.6163 -0.080889 -0.62512 0.92283 0.32611 -0.17706 -0.72904 -0.73523 -0.74999 -1.1757 -0.64919 2.0003 1.2264 0.074794 0.0086225 0.50118 -0.0087397 0.21278 1.2777 -0.58979 -1.5159 0.2774 -0.094495 0.023766 -0.044059 -0.57504 0.1823 -0.24189 -0.047133 0.12137 -0.62685 -gathered 0.6918 0.46456 0.25503 -0.60241 0.79194 -1.2784 -0.8563 0.4075 -0.49397 -0.48323 -0.58388 -1.2705 0.59665 0.016425 0.32 -0.14905 -0.10914 -0.36721 -0.31685 -1.1137 0.67754 0.76555 0.18503 -0.080153 0.014235 -0.75852 -0.2871 -0.026317 -0.8177 -0.6625 2.1595 0.71509 -0.43876 -0.93483 -0.5452 0.19489 -0.08888 -0.27393 -0.10094 0.66931 -0.16804 0.15483 0.091212 0.13637 0.91039 0.1102 -0.72015 -0.31027 -0.64397 -1.4194 -bombings 2.1611 -0.22174 0.3826 0.072347 -0.30503 0.6322 0.12474 -0.12337 -0.17079 0.69789 -0.82484 -1.5863 -1.4935 0.2113 0.4753 -0.14861 -0.32198 -0.41618 -1.0549 1.2035 0.82988 0.53839 0.061991 0.64917 -0.79576 -1.3367 -0.31513 -0.038324 0.48515 0.15363 1.6904 0.37065 0.020495 -0.86879 0.71298 -0.19614 0.57067 -1.3036 -1.4087 1.5417 -1.4323 0.18627 0.43587 -0.96082 1.3738 0.21866 -0.94913 0.61004 0.58623 -0.70055 -stressed 0.63547 0.40799 -0.60372 0.018168 0.4506 -0.25776 0.35515 0.12092 -0.16407 -0.48673 0.32878 0.50106 -0.56556 -0.2971 -0.044338 0.60654 0.31467 -0.5487 1.3734 -0.16363 -0.41604 0.31078 0.14231 -0.76104 0.87804 -1.2643 0.43501 0.28479 -0.61209 0.48001 2.4362 0.51863 -0.42636 -0.53509 -0.4941 -0.62507 0.091929 0.37477 -0.58159 0.47756 -0.3894 0.00044687 0.17461 -0.64608 0.67708 -0.21276 -0.8572 1.3323 0.15718 0.50442 -unknown 0.89855 0.30093 0.38384 -0.07748 1.2406 0.6338 -0.49759 0.59377 -0.16398 -0.079284 0.6614 -0.17841 0.064431 0.15498 0.63783 -0.12535 -0.045814 0.084162 -0.84272 0.25469 -0.53641 0.058337 0.53229 0.60801 0.41529 -1.2192 -1.1077 -0.29251 0.50284 0.65703 2.2331 -1.2356 0.18461 -1.1709 0.56209 0.3741 0.24536 -0.21032 -0.35088 0.20336 0.098822 -0.15596 0.088795 0.17909 0.21729 -0.50994 -0.48693 -0.07791 0.55245 -0.62789 -contest -1.0544 0.17442 -0.61171 0.84652 -0.18051 0.83898 -0.10609 0.49791 -0.040437 0.38875 -0.53082 -0.99765 -0.17308 0.36782 0.88101 -0.58343 0.27638 0.12945 -0.77052 -0.63414 0.65267 -0.095162 0.56087 0.60212 -0.019852 -1.3975 -0.53862 -0.30054 -0.38275 -0.19634 2.0036 0.39643 -1.2495 -0.5768 -0.15113 -0.14352 0.07366 0.75881 -0.85613 -1.1205 -0.17962 -0.039498 -1.0858 0.059687 -0.5739 -0.15127 0.064081 -0.41143 0.25715 0.025177 -infrastructure 1.1262 -0.057038 1.3764 0.74608 -0.6733 -0.11549 -0.18643 -1.1408 0.35623 -0.10685 -0.011641 0.0070104 -0.37482 -0.49327 -0.88969 0.61557 0.65879 0.93962 1.2277 0.41771 0.66949 -0.11447 -1.4071 -0.77954 0.17497 -0.18025 0.54913 -0.45724 0.87528 0.82944 3.5319 0.54741 0.50809 -0.72038 -0.34245 1.3338 -0.16118 0.38579 -0.072369 0.39212 -0.48109 -0.34421 0.53971 -0.39396 -0.33174 0.2935 -1.0257 0.77809 0.67347 0.17777 -manhattan 0.66727 0.53888 -0.24045 -0.30581 0.65174 -0.31216 -0.93497 -0.091959 0.0042352 0.036876 -1.253 -0.81184 -0.31233 0.62508 -0.016545 -0.037461 -0.29783 0.14964 -0.22693 -0.085759 1.534 0.52262 -1.2696 0.65569 -1.0788 -1.4145 -0.33598 0.48659 -0.16873 -0.80271 1.6401 -0.48395 0.59128 -0.86816 0.19624 -0.55622 0.048599 0.32337 0.98972 -0.09186 -0.62684 0.28687 0.23869 0.32888 -0.32001 0.4261 -0.18895 -1.4476 -0.11737 0.81329 -phase 1.0616 0.27443 -0.2528 0.43043 -0.53182 0.13682 0.86492 0.091777 0.21752 0.41272 0.2719 -0.6464 -0.48479 0.24896 0.096044 0.37701 0.38119 0.024887 -0.59315 -0.10459 0.2573 -0.96704 -0.099608 -0.28716 -0.208 -0.22737 0.23226 -0.095373 0.40784 0.82564 3.0427 -0.3796 -1.1551 -0.47474 0.48415 -0.011721 0.77371 0.62799 -0.23096 -0.12403 -0.61164 -0.86338 -0.38423 -0.16848 -0.32631 -0.14538 1.3724 0.40972 -0.23772 0.36404 -1972 -0.95028 0.44669 -0.59466 -0.083077 -0.85908 0.5948 -0.78597 -0.51968 -0.33109 -0.40816 0.39593 -0.1996 -0.58366 -0.060241 0.67092 -0.35635 -0.22396 0.6657 -1.2615 0.92944 0.60576 0.050329 0.34597 -0.36046 -0.34344 -1.2014 -0.096948 -0.54188 -0.32705 0.22635 1.7553 -0.78572 -0.9535 -0.60507 -0.11061 0.0015253 0.67615 0.29881 -0.20628 -0.15334 -0.62306 -0.62909 0.002715 -0.73523 -0.93219 0.38668 -0.35853 -0.6611 -0.59611 -0.11629 -brain 1.236 0.063412 0.54417 0.13424 0.12046 1.7931 0.65165 -0.011122 0.87405 0.54081 1.189 0.1931 0.26508 0.62263 -0.14265 1.0268 -0.71484 -0.025693 -0.017131 0.72811 -0.57257 -0.027933 0.84742 -0.1485 0.99638 -1.0532 -0.60337 -0.041229 0.27335 -0.47829 2.6488 -0.067772 0.59438 -1.5526 -0.60759 0.84716 0.57738 0.54409 1.5652 -0.052741 -0.56669 0.048863 -0.94652 0.62236 0.84841 0.081286 0.95803 -0.34797 -0.093328 0.36927 -ad 0.73545 -0.20366 0.84343 -0.59992 -0.029382 -0.64087 -0.17108 -1.5986 -0.23101 -0.16333 -1.4244 -0.22149 -0.42748 -0.92007 0.28848 -0.60219 -1.0145 -0.13866 -0.37326 0.57086 0.32476 -0.86213 0.70783 0.54014 -0.12654 -1.2655 -1.4601 -0.44288 -0.11567 0.097889 1.9467 -0.2031 -0.004633 0.80292 -0.58338 -0.72682 -0.010253 0.30922 -1.1871 0.15144 0.92496 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-pm 0.099233 1.07 0.3803 0.93857 0.49384 -0.50796 0.4938 0.56484 -1.7076 -1.4073 -0.8301 -0.7362 0.56314 0.75226 0.86133 0.50347 -1.307 0.27437 -0.94767 1.0605 1.0728 0.46235 0.40822 0.95895 0.94332 -0.66879 1.7519 -0.030067 0.43747 1.5924 2.2895 0.52324 -1.6884 0.79523 -0.72973 -0.3434 0.37356 -1.4511 0.26664 -0.40999 0.6533 0.025033 -0.27984 -0.5638 0.067134 1.3246 0.31583 0.69912 1.2508 0.34175 -meets -0.2895 1.3783 -0.20544 0.15446 0.58761 -0.29829 0.17945 0.33257 -0.025393 -0.70955 -0.19776 0.29579 0.021737 0.79495 0.38854 0.20544 0.084197 0.20491 0.45502 0.22252 0.24576 0.0963 -0.038147 0.746 0.327 -1.4717 0.51511 0.054886 -0.83236 0.23336 1.8947 0.097754 -0.88162 0.50402 0.26818 -0.37638 -0.093331 -0.044161 -0.16333 -0.72828 -0.3715 0.33405 0.21367 -1.1276 0.097266 0.47336 0.426 -0.33092 0.37571 0.32223 -bankruptcy 1.2289 -1.1894 0.31335 0.28215 -0.6635 0.69024 -0.4765 0.9557 0.089583 -0.18314 -0.20213 0.61085 -1.0281 0.18825 0.95105 0.18259 -0.99007 -0.70789 0.31278 -0.56127 1.0697 -0.73749 -1.3572 -0.39115 -1.2481 -1.5599 -0.18175 -0.39955 0.28018 -0.028922 1.78 -0.48472 0.10436 -0.20573 -0.40966 -1.0449 -0.19002 -0.91323 1.3744 -1.3184 -1.0299 -0.70584 0.69036 0.70978 -1.2742 0.51071 -0.19116 0.15882 -0.23654 0.37452 -delayed 1.0818 -0.32357 0.052324 0.27751 -1.1769 0.26938 -0.43575 1.1288 0.46263 -0.041264 -0.15786 -0.45593 -0.41501 0.26744 1.1408 0.45391 -0.97375 -0.71582 -0.24204 -0.567 1.0176 -0.046865 0.023704 -0.45655 0.18864 -0.8189 0.61066 0.19365 0.42211 0.81818 2.396 0.23027 -0.62848 -0.28501 0.38705 -0.13204 0.99308 0.14975 -0.53762 -0.099849 -0.81979 0.0077629 -0.15765 -0.16054 -0.55209 0.10451 0.066806 0.80015 -0.30553 -0.031766 -performing -0.059091 -0.029729 -1.1754 -0.53808 -0.11957 0.13549 -0.48836 0.1582 0.22211 0.89595 0.52541 0.053316 0.050771 0.60167 -0.028891 -0.42569 0.11693 0.2769 -0.464 -0.30924 1.0623 0.56639 -0.14468 0.10021 -0.21196 -0.33338 -0.63194 -0.76549 -0.59973 -0.75636 2.976 0.48055 0.97144 -0.80226 0.36377 0.4119 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-0.060476 1.0687 -0.35795 -0.16493 -0.045479 -0.7559 0.81442 -0.9838 -0.57234 0.0068391 -0.39852 -0.70665 0.42347 0.24815 0.50146 0.43068 2.8563 0.14494 0.75674 0.42768 0.41475 -0.28218 0.30992 0.03981 -0.23768 -0.21099 0.14881 -0.08812 0.18759 1.0134 -0.90363 -1.203 0.59133 0.18598 0.26841 0.23442 -oldest 0.057928 1.1348 -0.39969 0.28423 0.72027 0.32756 -1.1656 0.13928 0.28701 -0.34113 0.44197 -0.36526 0.2234 0.36148 0.096124 -0.16703 -0.040158 0.79102 -0.26848 0.66784 -0.3056 -0.37603 -0.72733 0.92035 -0.025554 -1.0794 -1.0379 -0.69089 -0.42505 0.34172 2.1543 -0.94353 0.39542 -0.097308 0.33316 0.17594 0.041185 0.74026 -0.3119 0.017778 -0.3521 -0.9502 0.22302 0.72686 -0.20501 0.78048 -0.92304 -1.0242 -0.22269 -0.93589 -priority 0.35595 0.67806 0.49411 -0.13684 0.33306 -0.15405 0.25301 -0.44937 1.0772 -0.55992 -0.1812 0.20247 -0.24215 0.13094 0.007562 -0.025013 0.19396 -0.25858 1.2132 -0.057249 -0.32017 -0.68227 -0.36452 -0.14171 0.079696 -1.1113 0.35335 -0.21206 0.30772 0.54126 2.619 1.0189 -0.45241 -0.68334 0.073744 0.15715 0.086717 0.28706 -0.58993 -0.35399 -0.68527 -0.52977 0.46549 -0.15813 -0.47064 0.25582 -0.2689 1.0924 0.66113 0.46492 -jumped -0.5146 -0.59154 1.6021 0.13429 0.88569 0.12137 -1.1581 -0.501 0.053087 0.37916 0.44519 -0.7906 -0.58164 -0.3144 0.35364 -0.049845 -0.96047 -0.21713 -1.2903 -0.4726 0.6284 0.3652 0.030999 -0.88635 -0.11406 -0.89586 0.57451 0.34915 0.18621 0.0041525 2.1329 0.6074 1.0759 0.94827 0.11631 -0.98714 -0.50029 -0.03737 0.91052 -0.5753 -0.57811 -0.18674 0.60172 0.0041713 0.066152 -0.46572 -0.054225 -0.49599 0.49307 -0.57215 -cool -0.65575 0.45659 -0.16748 -0.58345 -0.23073 -0.78348 -0.23166 -0.022452 -0.57968 0.526 -0.2214 0.17614 0.46513 0.79142 0.017403 1.0879 0.24418 0.27523 -0.26452 -1.0389 0.014045 0.68459 0.98151 0.21561 0.36278 -0.51819 -0.40552 1.349 1.5399 0.60541 2.6604 0.074535 -0.076292 0.12501 -0.026268 0.16843 -0.41844 0.44505 0.25033 -1.1557 0.24575 0.41847 -0.10633 -0.28433 0.51215 0.51371 0.53004 -0.889 0.054744 0.78793 -racial -1.4752 0.0072056 -1.0018 0.086084 0.39493 2.066 1.0421 -1.7136 -0.91602 -0.43872 -0.33302 -1.5592 -0.10399 -1.7235 0.14981 -0.079772 0.055223 0.0052731 0.079362 -0.30767 -0.60414 0.68349 -0.20261 0.023264 0.097872 -0.99327 -0.038139 0.40009 0.82943 -0.076038 2.1648 0.29325 -0.54347 -2.1429 -1.1387 -0.27231 -0.74225 -1.6584 -0.29622 0.34791 -0.5161 0.010968 0.1458 0.70908 -0.17632 -0.30487 0.40048 0.42701 0.23526 -0.59442 -window 0.671 0.54311 0.29415 -0.63575 0.83018 -0.41634 -0.44106 -0.30563 -0.058522 -0.27404 -0.36557 -0.23126 -0.30265 0.8775 0.31363 0.7269 -0.86038 -0.58385 0.0091684 -1.2716 0.63286 0.25587 -1.4147 0.23315 -0.026914 -0.53057 0.32484 1.3938 0.61097 -0.41879 2.4428 -0.28673 -0.026513 -0.21118 -0.084848 0.85286 1.0359 -0.22112 0.33854 -0.67272 0.50597 0.065792 -0.4784 0.34359 0.097306 0.16625 1.1851 -1.1885 0.51436 -0.75962 -aboard 1.5426 0.2891 0.97213 -0.41887 0.10394 -0.40224 -1.2014 0.82716 0.63278 -1.0635 0.10706 0.31496 0.27759 1.2792 0.06647 -0.86509 -0.7825 0.92325 -2.0466 -0.50776 0.2118 1.1612 0.22638 -0.27832 0.94466 -1.5619 0.37197 -0.0060163 0.41857 0.32703 1.2789 0.35475 -0.8687 0.44861 0.79942 -0.10673 0.45077 -0.13518 -0.35226 0.38941 -0.13584 -0.091332 1.4167 -0.26476 0.027383 -0.032732 -0.55857 -0.52434 0.025294 -0.44467 -heritage -0.36344 1.1691 -1.0041 0.46722 0.56243 -0.18086 -1.4175 -0.72645 0.61499 -0.6066 0.16354 -0.038164 0.9027 -0.54823 -0.42197 0.028926 0.82868 0.14116 -9.4151e-05 0.39213 0.18932 -0.1528 -1.329 -0.076394 0.2504 -0.74379 -1.6543 -0.51171 -0.31186 -0.30186 2.083 -0.24974 0.017228 -0.66011 -0.20752 0.14588 -0.16566 -0.2107 -0.38049 0.18579 0.08607 -0.91596 0.42344 -0.034631 -0.5542 0.73605 -0.92304 0.23536 0.21987 -0.88245 -apparent 0.80216 0.50893 0.10023 0.03958 0.40211 0.95821 0.58028 0.79847 0.034553 0.45539 -0.12933 -0.35387 -0.73998 -0.32531 -0.066092 0.071079 -0.41824 -0.71289 -0.50783 -0.15921 -0.26671 0.2811 -0.43224 -0.70882 0.38255 -1.8534 -0.47301 0.38433 0.88459 0.79963 1.9653 0.14332 -0.21406 -1.0562 0.19171 -0.13648 -0.12781 -0.51416 -0.57346 -0.041385 -0.27577 0.29884 -0.69027 0.0031585 0.2291 -0.41015 0.059856 0.80101 0.63272 -0.5553 -knee -0.21882 -0.052644 -0.71709 -0.37229 -1.0281 1.1978 -0.30328 0.77862 0.25025 -0.62951 0.78612 0.088139 -2.0231 0.10228 0.75425 0.91266 -0.91146 -1.0711 -0.48096 0.14938 -1.9889 0.62956 -0.057451 -0.911 -0.25967 -0.95332 0.7802 1.23 0.62747 -0.40826 2.3747 1.0017 0.79222 0.50754 0.41479 1.443 1.3825 0.824 1.006 -0.53828 -0.86842 0.32278 -0.42077 0.19046 0.36496 -0.30488 1.0468 -0.2758 -0.45969 -0.014299 -d.c. -0.71576 0.61225 0.039726 0.056933 -0.00096411 -0.84417 -0.8609 -0.004317 0.038666 -0.59446 -0.70068 -0.48908 -0.1046 0.1954 -0.091484 -0.49763 -0.50498 -0.12419 -0.4476 0.22767 0.77162 0.20001 -0.6088 0.35828 -0.77521 -1.4933 0.016274 -0.35509 -0.0019966 -0.75245 1.1923 0.045736 -0.18919 -1.5979 -0.37348 -0.46234 0.17033 0.082817 0.42354 0.098441 -0.21301 -0.081602 0.53939 -0.1113 -0.43734 0.88249 -0.28709 -0.56948 -0.1674 0.94631 -extend 0.40323 0.52704 0.25258 -0.16589 -0.30478 -0.13772 -0.15947 0.37508 0.39097 -0.0042925 -0.063256 0.098373 0.086359 -0.010366 -0.36742 0.72666 -0.11143 -0.89256 0.50194 -0.14328 -0.0070773 -0.2422 -0.51935 -0.55502 -0.27036 -1.0444 1.0087 -0.23107 0.25935 -0.089709 2.8373 0.76722 -0.8645 0.22818 0.14477 -0.48632 0.066502 -0.28083 -0.52124 -0.45816 -0.39207 -0.32314 0.3585 0.1688 -1.0197 0.070028 0.27272 0.3799 -0.63269 -0.27766 -rounds -0.27616 -0.38205 0.41911 0.53353 -0.47492 0.35176 0.2349 0.95737 -0.55859 -0.3292 -0.79933 -1.7662 -0.96369 0.23062 0.33061 -0.5622 0.44442 0.13386 -1.6026 -0.96977 0.031093 0.41209 1.2158 -0.31774 -0.5248 -0.33231 0.36684 0.044073 0.13402 -0.35186 2.6569 0.084429 -0.68996 0.67197 0.85104 0.57972 0.88665 0.99701 -0.76116 -0.26105 0.16509 -0.37095 -0.11717 0.23241 0.46 -0.34851 0.47075 0.53466 -0.74621 -0.31507 -devices 0.74791 -0.95048 1.8433 1.0673 -0.43163 0.46478 0.035884 -1.3014 0.84924 0.85318 0.61652 0.010733 0.33541 0.42747 -0.63614 0.95857 -1.2732 0.37623 -0.21977 -0.98506 0.77699 0.16499 -0.17523 -0.099818 0.34853 -0.90366 -0.34749 -0.72489 0.83126 -0.56723 2.6483 -0.29907 -0.11557 -1.0147 0.73908 0.95622 0.73619 0.30486 -0.54399 0.30093 0.72267 0.41258 0.052361 1.2179 0.96278 0.47824 0.64715 0.18052 -0.11499 -0.32259 -entirely 0.53927 -0.61627 -0.42906 -0.4556 0.24502 0.70329 -0.40132 -0.030627 -0.17069 0.064857 0.21024 0.063993 0.2243 -0.09391 0.14366 0.45125 0.48252 0.37167 0.25866 -0.31348 -0.0044782 -0.048455 0.19432 0.41203 0.52074 -0.93275 -0.68747 0.72482 0.72174 -0.074408 2.9507 -0.98681 0.33796 -0.88376 -0.13527 0.13435 0.46658 0.44954 -0.89976 -0.27797 -0.049156 -0.75837 -0.0075817 0.37868 -0.86873 -0.03837 -0.58508 0.068107 -0.03189 0.083492 -investigating 1.2636 -0.77308 -0.11829 0.67353 0.051432 0.59659 -0.0052356 0.32351 0.45643 -0.9462 -0.27419 0.083843 -1.0571 0.18516 1.0563 -0.73998 -0.39541 0.0089355 -0.1156 -0.34381 0.73096 0.42841 0.38711 0.0048358 -0.87167 -2.0327 0.014793 0.0046341 -0.59641 -0.097009 1.3882 -0.82199 -0.43669 -1.7905 0.081936 0.17681 -0.31481 -0.28643 0.61612 0.29552 -0.60667 0.27739 1.0015 0.43299 0.47679 -0.55488 -0.59105 1.1014 0.73296 -0.27085 -map 0.74059 0.65832 0.58353 0.40779 0.22541 -0.32611 -0.4973 -1.1128 0.29591 -0.9208 -0.17458 -0.33683 -0.026638 0.020408 -0.13655 0.56244 0.11543 -1.131 0.10523 0.20443 0.19491 -0.56532 0.28017 0.50403 0.48807 -0.11717 -1.2533 0.18678 0.80768 -0.41254 2.1727 -0.76983 -1.3894 -1.0795 0.11396 0.088149 -0.03848 -0.0474 -0.42951 0.24956 0.44222 -0.26072 0.23382 -0.91382 -0.89881 0.61954 1.2815 0.13363 0.64787 -0.48237 -backing -0.0894 -0.24263 0.35735 -0.28695 -0.40461 0.9351 -0.30882 0.39983 -0.37594 0.84282 -0.13405 -0.17524 -0.21438 0.15294 -0.79998 -0.3021 0.6022 -0.50118 0.85276 -0.8092 -0.074682 0.51693 0.093753 -0.54776 -0.50932 -1.1868 0.24937 -0.098079 -0.74727 0.061667 2.6631 0.13533 -0.60875 0.00059606 -0.13742 -0.21068 -0.1535 -0.4391 -1.2279 -0.86745 0.49574 0.57282 -0.62917 -0.99217 -0.085464 -0.10582 -1.1746 0.44076 -0.89161 -0.30526 -deployed 1.2805 -1.2172 1.1278 -0.44982 -0.12973 -1.2214 0.086085 0.17993 0.4024 -1.7883 0.74287 -0.77056 0.22475 -0.18219 -0.61257 0.33988 0.1599 0.66913 -1.129 -0.35126 -0.18731 -0.10743 0.1591 -0.27441 -0.76649 -0.85794 0.45987 -0.32447 -0.088069 0.32122 2.3506 0.53953 -0.74575 -0.67977 0.23359 0.9029 0.25574 -0.41205 -1.3189 0.1743 0.10634 0.22666 0.87236 -0.97435 -0.048312 -0.189 -0.036793 0.075626 -1.2913 -0.33155 -wish 0.38873 0.68049 -0.11749 -0.45191 0.99853 -0.34435 -0.26952 0.8861 -0.20737 0.18808 0.1496 0.99848 0.09442 -0.29505 0.70482 0.67314 0.21501 -0.23487 0.94706 -0.40527 -0.17464 0.55164 0.2412 0.15992 1.1426 -1.3272 -0.80885 -0.5366 0.62452 -0.84949 2.2279 1.2528 -0.96584 -0.17265 -0.17644 -0.42595 0.31559 -0.24597 -0.46896 -0.45215 -0.075895 -0.67195 -0.15794 -0.057187 0.080128 0.24004 -0.58598 0.19299 -0.21329 0.33807 -scenes 0.89474 0.26706 -0.99777 -0.093077 0.14339 0.34458 -0.0051794 -0.2547 -0.51263 0.37543 -1.2275 -0.37153 -0.20187 0.63935 0.91736 -0.57412 0.17077 -0.34531 -0.50615 -0.67176 0.79498 1.1087 0.070651 0.34338 0.5316 -0.32676 -0.98818 0.42343 0.56826 -0.38953 2.3771 -0.164 0.33474 -1.4971 -0.054898 1.3542 0.075598 -0.4412 -1.2376 -0.32686 -0.14877 0.71051 -0.16225 -0.28927 0.13426 0.008736 0.60883 -0.66815 -0.2774 -0.23598 -r. 0.081246 0.34031 0.34475 0.51022 0.54292 0.135 -0.13632 -1.0095 -0.57787 -0.68861 -0.92869 1.5086 -0.70036 -0.77543 0.49168 -0.53327 0.10972 -0.019829 -0.37005 0.16384 0.0058638 0.074664 0.7496 -0.85172 0.14145 -0.79943 0.43356 -0.84533 -1.2137 0.10089 0.87696 -1.8943 0.14078 -0.81471 0.29266 -0.4259 0.3376 0.29091 0.54524 0.3632 -0.11715 1.3459 -0.1382 -0.56662 -0.35151 0.89356 -0.42718 -0.9663 -0.7595 1.5289 -estimate 0.50778 -0.52357 1.4747 -0.56949 0.82821 -0.34716 -0.63702 -0.81914 0.43855 -0.18214 0.37968 -1.056 0.61771 -0.88316 1.254 0.0023669 0.083081 -0.83178 -0.77781 -0.68997 -0.078372 -0.75302 0.47081 -1.0053 0.11386 -0.35577 -1.0037 -0.23746 -0.63274 0.59913 2.5667 -0.56473 0.27227 -0.35408 -0.0084721 -1.0624 0.083864 0.006607 0.76325 -0.3627 -0.30767 -0.36259 0.7198 0.27072 -0.33213 -0.79315 0.17106 1.7242 0.16801 0.21063 -complained -0.092062 -0.65331 -0.037079 -0.30708 -0.54896 0.24169 -0.65739 0.75034 -0.20475 -0.29297 0.060386 -0.25141 -0.17454 -0.33825 0.53116 0.0039552 -0.40563 -0.244 0.71526 -0.89691 -0.23989 0.68944 0.24846 0.018574 0.077084 -1.7359 0.25156 0.22238 -0.37498 0.2407 1.9166 0.35592 0.29748 -0.66794 -0.43078 -0.18306 0.16825 -0.0011045 0.049896 0.4213 -0.11464 0.85839 0.19574 0.49765 0.61459 -0.40439 -1.3942 0.58976 -0.20955 0.079396 -effectively 0.32683 -1.4344 0.27745 -0.42882 -0.10285 0.50276 0.27541 0.18559 0.13196 0.1768 0.45245 0.30338 -0.42941 0.03609 -0.1079 0.60719 0.29597 -0.43525 0.49036 -0.11758 0.39626 -0.48117 0.045031 -0.12264 -0.149 -1.4089 0.73955 -0.29051 0.66949 -0.024838 2.7412 0.054969 -0.46663 -0.64308 -0.53922 0.14651 0.223 0.24565 -0.68468 -0.18335 -0.39609 -0.42028 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0.11525 0.36242 -1.7711 0.491 -1.1569 -1.0901 0.6059 1.3989 -0.89633 -0.84213 -0.61894 -1.3335 -0.61563 -0.36243 -0.65133 0.22474 0.449 -0.26256 0.64698 -0.16885 -0.26816 1.291 -0.95191 -1.3975 -1.0577 0.16917 0.02875 -fail 0.45682 -0.60629 0.85184 -0.58558 -0.54939 0.21787 0.27852 0.69506 -0.24624 0.26122 0.25349 0.42472 -0.32273 0.18626 0.31701 1.1981 0.72521 -0.49341 0.68367 -0.89897 0.060898 -0.18691 0.3107 0.21246 0.43193 -1.1001 -0.39925 -0.6418 0.32036 -0.22346 2.3266 0.91371 -0.91684 -0.4748 0.24238 -0.13437 0.45967 -0.17704 0.028216 -0.70075 -0.55855 -0.61886 0.52355 0.34227 0.22245 0.4687 -0.041167 0.7928 0.29798 -0.078627 -funded 0.94193 -0.17658 0.47265 -0.65209 -0.74741 0.22534 -1.004 -0.99758 1.007 0.34719 0.029922 -0.043628 0.6328 -0.18383 -0.34391 -0.34034 0.6225 1.0088 0.59052 0.95362 1.0702 -0.32929 -0.14773 -0.43104 -0.67742 -1.1536 0.19959 -0.81822 -1.2897 -0.24229 2.4123 0.045922 -0.053888 -0.54513 -0.58733 0.2378 0.064686 0.81486 -0.1331 -0.50784 -0.19085 -0.71131 0.36495 0.49895 -0.39829 -0.75959 -1.3877 0.63534 -0.27401 0.49865 -categories -1.1283 0.6793 0.013473 0.60647 0.33602 0.70111 0.4005 -1.4341 0.24425 0.0065502 0.14426 -0.073666 -0.074548 0.4954 0.45724 -0.65416 -0.37208 -0.35114 0.34745 -1.1197 0.50921 -0.41328 0.94149 0.67678 -0.74348 0.2132 -1.4881 -0.6706 0.00075784 -0.071506 2.8661 0.41085 0.66047 -0.73811 0.61767 0.31657 -0.21148 0.1528 -0.69596 -0.070236 -0.23463 -0.25897 1.037 0.3815 -0.61015 -0.32109 0.2473 0.39866 0.47907 0.1881 -difficulties 1.0462 0.23593 -0.32645 -0.083061 -0.68883 0.84736 0.1579 0.70148 0.081924 -0.42907 -0.0070297 0.43275 -0.87941 -0.66499 0.39073 0.076846 -0.22647 -0.18592 0.83175 -0.52594 0.41867 0.17217 -0.39654 -0.28685 1.0446 -0.74022 -0.11908 -0.38874 0.55562 1.0967 2.7149 1.0124 0.66632 -1.1679 -0.1792 0.49619 -0.64793 0.099514 -0.27283 0.12657 -0.71078 -0.32212 0.16907 0.30957 0.0684 -0.11276 0.098547 1.0892 -0.014188 -0.41135 -exit 0.63117 0.5875 1.0619 -0.060472 -0.82182 0.0052793 -0.23241 0.74094 0.12161 -0.23601 -0.89879 -0.89674 -0.58095 0.41007 -0.23764 -0.10794 -0.089622 -0.80374 0.057256 -0.23965 0.18978 -0.082673 -0.21125 0.67281 0.35825 -0.70726 0.082086 0.50967 -0.12267 -0.26305 2.1561 0.32682 -0.15033 -0.39192 0.20012 -0.55716 1.1685 -0.064682 -0.43993 -0.77375 -0.0089247 -0.67546 0.16019 -0.038465 -0.87461 0.086011 0.30535 0.17129 1.1428 0.15847 -negotiate 0.82453 -0.18651 0.12889 -0.20175 0.14613 -0.18342 -0.36718 1.1917 -0.59295 0.15081 0.1465 0.57429 -0.27628 0.26303 0.13958 0.82852 0.78703 -0.61648 1.4013 -0.68638 0.41442 0.10259 -0.2211 0.15901 -0.45713 -1.1237 0.858 -0.38112 0.48522 -0.1771 2.3498 0.19702 -1.8426 0.35598 0.49893 -0.84071 -0.32713 -0.50978 -0.63486 -0.18871 -0.40762 -0.42349 0.34041 -0.087841 -0.35168 -0.81976 -0.18608 0.95982 -0.65345 0.024652 -google 0.969 -0.61799 1.6561 1.4079 -0.063774 -0.45934 -1.0961 -1.2889 0.51762 0.48351 0.0055815 0.27758 -0.63178 -0.41388 0.41559 0.27384 -0.90019 -0.48922 0.38597 -0.81652 0.59012 -0.41399 -0.27623 0.32718 0.59101 -0.91574 -0.2532 -0.43769 -0.51452 -0.7086 1.8017 -0.55229 -0.41493 -0.64137 -1.0188 -1.1337 -0.77804 0.0015144 -0.81687 -0.80404 1.1654 -0.35197 -0.39405 0.63104 -0.0911 -0.51041 0.65359 0.1184 0.29559 0.18361 -verdict 0.064504 -0.63358 -0.45334 0.36075 1.1578 0.016751 1.4115 1.2411 -0.021315 1.0622 -0.68543 -0.77755 -0.34901 -0.26119 1.4501 -0.078005 -0.73951 -1.0685 -0.12893 -0.52714 0.79214 0.98448 0.25932 -0.47175 -0.0056314 -1.8428 -0.25451 0.40404 -0.45924 -0.031704 1.4578 -0.7369 -0.49737 -0.44352 0.064319 -1.2074 1.3185 -0.38098 0.044557 -0.84457 -0.24411 0.34904 -0.041544 0.22884 -0.27001 0.52503 -0.60682 0.37387 0.28711 -0.12041 -conversation 0.55757 0.73057 -0.60744 0.29209 0.70341 0.0157 -0.52776 0.69176 -0.76899 0.12439 -0.84008 0.36278 -0.38898 0.69902 0.74704 -0.26093 -1.0068 -0.13428 0.49166 -0.35737 0.65272 1.2923 0.81358 0.49689 1.2772 -1.3159 0.44234 0.65405 0.5313 -0.012682 2.1589 -0.25534 -0.5798 -0.66636 -0.05259 -0.12432 -0.30012 0.373 -0.51449 0.28095 0.45695 0.58785 -1.1294 0.47395 0.27209 -0.37777 0.55338 0.48503 -0.44908 0.53669 -plate -0.63773 0.83063 -0.26741 0.92461 0.47251 0.29511 -0.31165 0.49289 -0.10176 -0.66635 0.20884 0.33366 0.51571 0.97914 -0.15615 -0.51083 -0.11238 -0.24715 -0.68595 -1.3808 -0.0767 -1.3366 0.37579 -0.13996 0.31216 -0.66554 0.29805 1.2569 0.94895 -1.1671 2.1986 -0.039899 0.057112 0.63481 -0.58989 0.48842 0.10233 1.0525 0.44279 0.17923 0.53623 0.16626 -0.16491 0.31953 -0.61487 -0.15407 0.57715 -0.30956 0.10163 -0.66864 -1950 -0.96776 0.34263 -0.63944 -0.53506 -0.49711 0.091293 -0.70896 -0.35747 -0.66246 -0.72954 0.5986 -0.098788 -0.32591 -0.30569 0.63054 -0.4465 -0.58624 0.78128 -1.4606 1.0541 0.68715 -0.17491 0.5051 -0.1291 -0.24492 -0.81775 -0.10404 -0.64622 -0.24023 0.53473 1.6265 -0.64799 -0.40632 -0.60995 0.019151 -0.070379 0.48924 0.4873 -0.12157 -0.026105 -0.45982 -0.63147 0.27889 -0.90078 -1.1189 0.40845 -0.25132 -0.93443 -0.40918 -0.013158 -totally 0.40695 -1.4406 -0.17813 -0.21023 0.36731 0.26126 0.14239 0.041061 -0.61054 0.30215 0.18146 -0.049216 -0.64714 0.028726 0.70163 0.72709 0.73611 0.54732 0.36596 -0.11867 -0.66232 0.88739 0.27436 0.32745 0.9479 -1.0829 -0.48233 1.179 1.3878 -0.11181 2.4607 -0.71156 0.17669 -0.60554 -0.88381 0.083859 0.85267 0.23708 -0.77943 -0.57827 -0.098225 -0.98837 0.55839 0.54079 -0.36776 0.17429 -1.2801 0.1565 0.68669 0.27528 -evil 0.76567 -0.007752 -0.28001 -0.22007 1.2617 0.44802 1.519 -0.49086 -0.53432 0.72989 -0.25435 1.5748 -0.5606 0.38719 0.036257 0.13234 0.69087 0.42111 -0.72543 0.61388 -0.53527 0.11526 0.57465 -0.0066877 0.41334 -1.7219 -1.1447 -0.53068 1.2494 -0.20739 1.4562 -0.011057 -0.69431 -0.75299 -0.71556 0.31185 -0.40735 -1.0304 -0.88434 -0.67811 -0.078053 -0.36073 -0.092511 0.3601 0.63368 0.73662 -0.42298 -1.1926 0.25628 -0.094014 -cent -1.4147 -0.2816 0.71488 -0.22112 0.71828 0.56504 -0.29248 -1.076 0.080334 0.67741 0.3296 -0.75631 1.13 -0.99193 0.25144 0.53383 0.36279 0.022839 -1.2883 -0.267 1.2096 -0.0059165 1.1735 -0.46735 -0.68256 0.46823 0.66507 -0.3396 0.71519 1.0335 2.6957 0.052581 0.28653 1.8955 0.49912 -1.5488 0.20655 -0.27285 0.61362 -0.59252 -0.10166 -0.69782 0.26164 0.094041 -0.21934 -0.61463 -0.78044 -0.065728 0.74129 0.6916 -canal 1.4404 0.87462 -0.22737 -0.29025 -1.6993 0.093995 -0.78204 0.57253 0.89677 -0.62964 0.062581 0.71978 -0.14871 -0.71274 -1.6365 0.20962 0.19035 -0.18887 -0.25446 0.7393 1.0584 -0.57131 0.033771 -0.094652 -0.30684 -0.92673 0.019853 1.0695 0.5324 -0.54737 1.9398 -0.22615 0.19586 0.36338 -0.17 -0.24729 0.21925 0.39983 0.4758 0.71992 0.70583 -0.48173 0.072501 -0.30095 -1.9297 -0.31221 -0.35681 -0.75546 0.46512 -0.88776 -harder -0.29399 -0.79617 0.67096 -0.66914 -0.19887 0.12998 0.092694 0.3473 -0.24361 0.52228 -0.1028 0.19513 -0.40637 0.2347 0.13594 0.78369 0.97587 -0.16608 0.67398 -0.99359 -0.11845 0.026627 0.27858 0.31713 0.66437 -1.4722 -0.11494 -0.10703 0.90995 -0.59183 2.4475 0.53444 -0.020802 -0.19736 0.12 -0.15844 -0.41524 0.52509 -0.074545 -0.6972 -0.67977 -0.030141 0.64788 0.31299 0.43638 -0.17408 0.35904 0.093447 -0.091229 0.47121 -diseases 1.7165 0.027674 -0.35055 -0.071342 -0.79714 1.556 0.72156 -1.15 1.7751 -0.36306 0.41025 0.30092 0.48448 -0.63042 0.2782 0.38131 -0.49301 -0.24156 -0.15222 0.83651 -1.136 -0.44895 0.98538 -0.065764 0.4027 -0.9197 -0.35989 -1.1481 0.1676 0.64755 2.3646 0.5581 0.70546 -1.3427 -0.5935 0.58831 -0.12602 0.57767 0.91282 0.50565 -0.92053 -0.15682 0.94994 1.3666 2.4785 0.91399 0.068568 -0.089361 0.087189 0.33738 -succeed 0.60958 0.11618 0.58789 -0.46775 0.5387 0.0071826 -0.31473 0.34352 -0.86256 0.051761 0.020275 0.97808 -0.99496 0.3563 0.28393 0.83647 0.70312 -0.038635 0.73156 -0.31103 -0.43645 -0.0515 -0.043858 -0.27961 0.56127 -1.6028 0.29596 -0.71254 -0.33542 0.81791 1.3738 0.58091 -1.253 -0.72997 0.29325 -0.626 -0.13116 1.225 -0.47444 -0.79243 -0.73134 0.21711 -0.60963 -0.2515 0.028094 0.27837 -0.75373 -0.24877 0.02573 0.8134 -eligible -0.435 0.34399 0.95092 -0.46483 0.49975 0.45529 -0.71291 0.64181 0.11425 -0.043923 0.57081 -0.81498 1.438 -0.0655 1.5049 0.025711 0.11049 0.029327 0.44422 -0.23008 -0.22427 -0.44489 0.1391 0.68277 -0.60596 -0.57875 0.49507 -1.3553 -0.5939 -0.27121 2.0595 1.0768 -0.78178 -0.72025 1.0807 0.082101 0.95267 0.28699 -0.056628 -1.0468 -0.92571 -0.34395 0.77182 0.85152 -1.6109 -0.81757 -0.76916 0.69903 -0.044432 0.42726 -taste -0.25826 0.20755 -1.8089 -0.0045707 1.002 0.30873 0.23493 0.13954 -0.16455 1.1608 0.12976 0.39695 0.9664 -0.22466 0.20641 0.22372 -0.043133 0.26844 -0.090198 -1.213 -0.20942 0.19578 1.1608 0.41065 -0.088691 -0.77222 -1.1368 0.98575 1.5428 -0.05683 2.2402 0.63528 -0.072622 -0.23851 0.29049 0.12906 -1.1139 0.89749 0.55485 -0.77596 0.71329 0.062237 -0.13661 0.19611 0.58233 1.5621 0.2034 0.23999 -0.032633 0.28185 -smoking -0.22847 -0.050469 -0.38089 -1.3181 -0.73702 0.90236 -0.025856 -1.1105 1.396 0.59017 -0.40876 -0.63721 0.21357 0.15316 1.0091 0.53183 -0.07662 -0.0866 -0.007077 -0.92934 0.33439 0.18787 0.31318 0.64406 -0.64933 -1.5377 0.083074 0.37145 0.86854 0.41274 1.5857 0.47493 -0.50243 -0.46675 -0.45499 -0.21738 0.65699 -0.080612 0.040776 0.26834 -0.463 0.19831 0.56643 1.4116 1.3545 0.12495 -0.67208 -0.88752 0.26679 0.22449 -legs 0.27915 -0.28937 -0.34266 -0.88186 0.57105 1.2378 0.44534 0.17197 0.39677 -1.0363 0.59049 0.26478 -0.81994 1.0415 -0.36155 0.22966 -0.24276 -0.21458 -0.63325 -0.90774 -1.4283 0.21093 0.95913 -0.41671 0.39432 -0.77383 0.41602 1.3137 0.32432 -0.78567 2.4089 1.2239 0.72811 0.92834 -0.43791 0.64528 0.31355 0.65695 0.011517 -0.7522 -0.44912 -0.19915 0.056439 0.47544 0.44388 -0.41885 0.7026 -1.4277 -0.26393 -0.67936 -somebody -0.41389 -0.3141 0.75711 -0.53476 1.2983 0.045347 -0.064086 0.61138 -0.055897 0.34971 -0.48176 0.51777 -0.57557 0.13876 1.0385 0.25242 0.43727 0.43248 0.24525 -0.66194 -0.21867 0.66661 0.29911 0.42407 1.3157 -2.1102 -0.76031 0.44223 1.3722 -1.5735 1.9085 0.59565 -0.5392 0.0029585 -0.42862 -0.00063402 0.41214 -0.065116 0.66985 -0.53707 -0.16015 0.039046 -0.43625 0.61921 0.10156 -0.12632 0.070854 -0.27276 0.31225 0.71988 -maintenance 0.88779 0.040191 0.12059 -0.291 -0.74041 -0.092428 -0.45533 0.089486 1.4317 -0.85637 0.33914 0.29064 -0.21941 -0.1741 -0.40472 -0.12749 -0.74903 1.0313 0.44694 -0.83615 0.60369 -0.33364 -1.1356 -0.71089 -0.38446 -0.56862 -0.1038 -0.20873 1.1165 0.61521 2.8645 0.018592 0.16638 -0.19843 0.64793 0.82263 0.87621 0.42425 0.74725 0.78294 -0.38389 -0.90346 0.43333 0.7097 -0.46292 -0.51246 -0.043438 0.58973 -0.071556 0.97467 -studying 0.29422 0.35414 -0.86546 -0.73286 -0.083424 -0.043359 -0.63079 -0.4231 -0.25952 0.26196 0.82022 0.087081 0.31363 0.25697 0.12994 0.13139 -0.2132 0.49243 -0.20708 0.23142 0.79596 0.68741 0.3804 0.086785 0.72587 -1.1142 -0.13817 -0.72487 -0.79564 -0.33821 2.2126 -0.29971 -0.3857 -1.5723 0.29289 0.15576 -0.45823 1.0286 0.58909 0.60666 -0.47303 -0.461 0.61692 0.55924 0.50318 -0.082257 0.96579 0.39226 -0.57527 -0.10731 -symbol -0.53621 1.2409 -0.40451 0.059426 1.3816 0.057993 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0.49411 -0.15024 0.067081 0.63838 0.8204 0.36131 -0.41445 -0.37536 0.048139 0.80063 -0.71477 -midnight 0.2138 0.0079131 -0.15382 -0.35416 0.063486 -0.95376 -0.63766 0.4436 -0.52033 -0.40668 -0.49475 -0.76738 0.092146 0.92561 1.1707 0.49476 -0.97375 -0.33804 -1.3283 -0.32446 1.1676 0.44931 0.33699 0.46373 0.084261 -0.29274 -0.058169 0.4436 0.87102 0.024441 2.0593 0.065058 -1.3766 0.49486 -0.27636 -0.32505 1.0408 -1.3189 -0.011004 0.44774 -0.21388 0.10719 -0.71321 -0.30577 -0.17133 0.22296 0.59091 -0.58368 -0.23963 0.079349 -unprecedented 0.32783 0.39305 0.034494 0.12025 -0.23668 -0.08094 0.68963 0.42531 0.53645 0.79932 -0.79884 -0.8394 -0.32682 0.098036 0.48492 -0.54307 0.3809 -0.5963 -0.7061 -0.24901 0.42113 -0.13265 -0.72712 -1.3936 0.23888 -1.0816 -0.18366 -0.26223 0.21644 1.2105 2.4605 0.70931 -0.14506 -0.4782 -0.61265 0.039088 -0.0048263 -0.36828 -0.86412 -0.55683 -0.8842 0.054415 -0.23644 0.12917 -0.29834 -0.1063 -0.21664 1.0152 -0.076826 -0.40616 -cooperate 0.92842 -0.70499 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0.36755 -0.2085 -0.089226 -0.75459 -0.099513 -0.69164 0.022976 2.2213 0.19255 0.092026 0.11685 -hide 0.78504 -0.28009 0.57888 -0.27366 0.65411 0.28256 -0.04281 0.1845 0.16893 -0.055116 -0.35455 0.61231 -0.47646 -0.33265 0.33501 0.60014 0.63198 -0.28997 0.37578 -0.42386 -0.062583 0.38458 0.64895 -0.072437 -0.41241 -1.4683 -0.85221 0.33668 1.4324 -1.0522 1.6694 0.036827 -0.35096 -0.42385 -0.19276 0.99937 -0.66929 -0.44264 0.059919 -0.6199 0.093728 -0.086567 0.51931 0.65949 0.85126 -0.89196 -0.103 -0.41622 0.17441 -0.687 -consisted -0.082754 0.19543 -0.32563 -0.25186 0.14517 0.58786 -0.34076 -0.029859 -0.14352 -1.1863 0.080321 -0.56388 0.21748 0.39464 -0.39371 -0.86838 -0.077329 0.65862 -0.73548 -0.48955 0.47294 -0.55918 0.34927 0.20894 -0.73676 0.24502 -0.41293 -0.35877 -0.29891 0.23472 2.557 -0.68986 0.10192 -0.15747 0.64197 0.96651 0.76519 0.32539 -0.87117 0.093117 0.12183 -0.68579 -0.17076 -0.29755 -0.56858 -0.051594 -0.085276 -0.5527 -0.67401 -0.040243 -edged -1.3779 -0.34962 0.13693 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0.57298 0.53109 -0.85421 -0.97823 0.21794 -1.0068 -0.1616 -0.28263 1.3123 1.1235 -0.29648 -1.0001 1.1829 0.97761 -0.38921 -0.30957 -0.36703 -0.39472 -0.18362 -0.019792 -0.13474 0.3195 0.90557 -0.89918 0.23391 0.6722 -0.64839 0.19555 -solving 0.83334 0.060155 -0.30612 0.31962 0.57138 0.77526 1.1508 -0.30785 0.0085042 0.050312 -0.29006 0.029528 -1.0774 0.2134 0.47146 -0.2844 1.0674 0.80246 1.164 -0.015791 0.22739 0.23487 -0.31913 0.6007 1.2315 -0.586 0.38347 -0.4982 0.40004 0.28455 2.1547 0.03362 -0.63163 -0.91486 0.14451 0.4907 -1.1781 0.30112 0.025706 0.4662 -0.45239 -0.24091 0.27486 0.2424 -0.045313 0.13111 1.5464 1.7154 0.1071 -0.28173 -patterson -0.94577 0.22284 -0.05286 0.49128 0.38291 0.096088 -0.7676 0.047432 -0.052058 -0.53605 -0.30965 0.53724 -0.62518 -0.53372 0.02708 -0.13662 0.17175 -0.1631 -0.093678 -0.03696 -0.51 1.0181 0.21631 -0.47668 0.19209 -0.46044 0.66728 -0.32784 0.020726 -0.29366 0.72162 -0.53292 -0.15388 -0.33806 0.24238 -0.2716 0.038153 -0.26653 0.59463 0.057724 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-0.38124 -0.30227 -0.11956 0.043688 -0.11759 0.53912 0.10773 0.23792 -1.0893 -0.26929 -woo 0.20931 0.22697 1.1484 0.010467 -0.68476 -0.14402 0.042576 0.029019 -0.6464 0.85858 -0.89326 0.30855 0.20095 0.61295 -0.84029 0.10079 0.92198 -0.047643 0.43564 0.36376 0.36697 0.0041855 0.47836 0.22802 0.42881 -1.647 0.151 -0.92745 -1.7034 -0.78642 1.0114 0.70979 0.086823 0.49267 0.023574 -0.89706 -0.72391 0.10228 -0.57951 -1.0237 -0.40307 0.63257 0.11687 -0.21894 1.5627 -1.3001 -0.18099 -0.43606 0.2352 0.54941 -1886 -0.5293 0.262 -0.75785 -0.9198 -0.35824 0.49321 -0.93516 -0.13624 -1.0784 -1.024 0.19296 0.031054 0.1001 -0.71431 0.17455 -0.39401 -0.86092 0.25039 -1.2273 0.69465 1.1978 -0.56944 0.20744 -0.096365 -0.051943 -0.31586 0.1029 -0.47663 -0.083213 0.7111 0.72968 -1.1603 -0.46246 -0.68088 0.075165 -0.38376 0.71112 0.66956 0.39549 0.39891 0.063748 -0.90875 -0.20579 -0.49553 -0.91152 0.56557 -0.47051 -1.2029 0.48044 -0.1813 -mortality 0.15235 0.65442 -0.031841 -1.8199 0.50773 1.174 0.47896 -0.95711 2.233 0.063145 0.57724 -0.73566 0.72137 -0.74322 0.88602 0.023002 -0.39098 -1.0651 -0.093272 0.35715 -0.75782 -0.20609 0.8479 -0.044205 0.64735 -0.11587 -0.78713 -0.4355 0.030895 1.5576 1.6162 1.0443 0.90992 -0.44765 0.17827 -0.46386 0.0051656 -0.14186 0.72946 0.059215 -1.3414 -0.22135 0.58127 0.31466 0.5527 -0.30859 1.1335 0.098543 0.52102 0.17294 -24-hour 0.10608 0.12827 -0.13616 0.20402 -0.8628 -0.89304 -0.39038 0.27379 1.1909 -0.036487 0.094257 -1.0925 0.31953 1.0697 0.53048 -0.098003 -1.2076 0.069447 -0.43369 -0.10256 0.83274 0.50587 -0.86912 0.83952 -0.078835 -0.16877 0.32848 -0.094794 0.76857 1.3011 2.3956 0.79092 -0.56517 0.8128 -0.51671 0.3042 0.8035 -0.57674 -0.40642 0.57928 0.30063 0.34046 -0.7281 0.69424 -0.44775 -0.65562 0.61682 0.43537 -0.32415 0.72287 -unicef 0.70869 -0.080496 -0.25652 -0.52459 0.12892 -0.044981 -0.44602 -1.011 2.5161 -1.0119 0.23397 -0.091001 0.098451 -0.89112 0.84184 -0.36314 0.31023 0.23974 1.0196 0.48434 0.02248 0.79932 0.5302 -0.33001 -0.0016008 -0.028841 0.49401 -0.25904 -1.5134 0.1423 1.2673 1.0321 -0.030115 0.12937 -0.81986 0.36562 0.36614 -0.25854 0.15293 -0.17174 -0.090688 0.10274 0.32681 -1.5637 0.85884 -0.11336 -0.77728 0.65527 -0.2866 0.45801 -teresa 0.69443 1.2647 -0.26742 -0.049317 0.22958 0.002397 -0.58899 0.22816 0.41045 0.22424 0.013854 0.11563 -0.32699 -0.57312 0.37932 -0.69801 -1.2392 -0.87268 1.2342 0.34293 -0.88669 1.0861 -0.018962 -0.38384 0.74396 -0.27753 -0.35172 -0.33822 -0.69769 -0.22448 0.45341 0.4131 -0.097772 -0.15051 -0.3203 -0.42121 0.34125 0.66544 0.84828 0.64947 -0.18813 0.47361 -0.33241 -0.68344 -0.056369 0.21789 -0.9641 -1.6525 0.54473 0.090824 -monuments 0.44328 1.4606 -0.53063 -0.13081 0.16517 -0.7287 -0.24554 -0.65914 -0.19594 -0.8163 -0.95108 -0.18329 0.51258 -0.55237 0.13574 -0.17511 0.96281 -0.42404 0.28792 0.17124 -0.028336 -0.85658 -0.94982 -0.89066 0.001647 0.14013 -1.9942 -0.37324 -0.2097 -0.55916 1.1577 -0.5052 0.2866 -1.2199 -0.49884 1.2554 0.65677 0.094366 -0.80656 1.008 -0.47953 -0.78453 1.012 0.46959 -0.37301 1.0108 -1.397 -0.43923 0.06202 -1.6743 -locate 1.5568 -0.29745 1.6059 0.2228 0.65743 -0.46483 -0.7365 1.226 0.82104 -0.41362 0.41276 0.28773 0.48261 0.46574 -0.27298 0.31187 0.34115 -0.34923 0.34254 -0.43775 -0.063089 -0.030012 -0.030301 0.37445 0.47335 -0.7558 -0.7614 -0.72262 0.83558 -1.0258 1.1862 -0.47594 -0.58674 -1.0937 0.64116 0.79917 0.0018519 -0.023035 0.21469 -0.11369 0.41401 -0.48381 0.85603 -0.047091 0.4557 -0.31808 0.14469 0.0097545 0.34127 -0.41602 -finalist -1.2112 0.76008 -0.079427 1.0558 -0.51555 0.017983 -0.36067 1.1264 -0.024716 0.064329 -0.11751 -0.50623 -0.80563 0.27099 1.6757 -0.94976 0.15206 -0.58575 -0.38383 0.32261 -0.42751 -0.45204 -0.19755 0.3271 -0.013837 -0.69194 -0.66666 -0.84979 -1.4652 -0.28958 0.79684 -0.21901 0.73632 -0.06419 -0.12507 0.5533 0.21981 1.7844 -0.066745 -2.1621 0.5951 0.073342 0.47038 -0.76272 -0.22569 0.23419 0.1685 -0.77733 -0.42614 -0.18597 -rooted -0.33969 0.17529 -0.97424 -0.089147 0.26986 0.93017 0.26018 -0.4186 -0.79034 0.47029 0.011976 -0.13561 -0.41353 0.15212 -0.5726 0.32148 0.28762 -0.12682 0.83292 0.52943 -0.85943 0.12508 -0.28077 0.34186 0.7325 -1.0211 -0.40887 0.44125 0.3854 0.55834 1.7824 0.079851 0.22635 -0.49813 -0.93282 -0.035598 -1.0816 -0.02753 -0.44097 0.080283 -0.37102 -0.64291 -0.25002 0.81339 0.52259 0.7326 -0.26016 0.39837 0.26775 -1.395 -35,000 0.44348 0.13462 1.3003 -0.90194 0.25574 -0.19733 -0.35025 -0.039782 0.23442 -0.34348 0.24225 -1.5082 1.3044 -1.2124 1.3455 -0.132 0.13828 1.1186 -0.49184 -0.48767 -0.039991 -0.41454 -0.2472 -0.024916 -0.39808 -0.31894 0.34125 -0.16427 -0.23603 0.19944 1.6188 0.47281 0.1916 0.52531 -0.42061 0.26696 0.16415 -0.3744 -0.15484 -0.24679 0.43802 0.20796 0.6675 0.43924 -0.62676 -0.78436 -0.51654 0.58407 -1.8664 -0.54009 -reaffirmed -0.10316 0.47036 -0.45432 0.045662 0.60475 -0.27226 0.55518 0.10318 -0.32326 -0.31166 0.2831 0.21609 -0.55112 -0.69291 -0.63569 0.58426 -0.46265 -0.87536 1.072 0.082789 0.064748 0.32111 -0.22552 -1.3845 0.19543 -1.1156 0.72808 0.065112 -0.84809 0.34811 1.1774 0.69081 -0.89113 -0.055735 -0.3298 -1.5764 0.11635 -0.12658 -1.1886 -0.20623 -0.14034 -0.84684 0.1234 -1.0848 0.091468 0.3938 -0.73728 1.4745 -0.096369 0.043857 -sided -0.23575 -0.58353 -0.29451 0.1822 -0.056345 0.52039 0.42193 0.50886 -0.58589 -0.066841 -0.76153 -0.72146 -0.36021 0.37497 -0.43997 0.12053 0.14557 -0.77021 0.39436 -0.34452 -0.34455 -0.1564 -0.051255 -0.14427 -0.42381 -1.194 0.013097 0.8496 -0.085972 -0.1092 1.253 -1.0696 -0.015936 0.23742 -0.26854 -0.37767 0.23314 0.49127 -0.9739 -0.42697 0.31642 0.19003 0.0026018 0.50121 -0.31095 0.4595 -0.48196 -0.050905 -0.5598 -0.66185 -stretches 0.49732 0.44986 -0.34336 0.077718 -0.44661 -0.0058371 -0.079091 0.059137 0.91488 -0.21201 -0.95841 -1.1243 -0.062117 -0.1644 -0.38541 0.0094256 0.63006 0.032086 -0.30121 0.34401 -0.74964 0.0065974 0.039079 0.32819 0.36573 -0.3888 0.13288 1.2388 0.88935 -0.43816 2.121 -0.25921 0.89443 0.21056 0.0010588 0.2599 -0.6504 0.49491 -0.34776 0.35296 -0.74987 0.27541 0.39712 0.51011 -1.3664 0.058397 0.8003 -0.84844 -0.66448 -0.68007 -convened 0.72683 -0.23344 -0.75687 -0.18495 0.11672 -0.72885 0.04428 0.51969 -0.62417 -1.1656 -0.75987 -0.54456 0.010806 1.0459 0.28318 0.40836 -0.31587 -0.65653 0.40404 -0.10359 1.1345 0.054871 0.60096 -0.36682 -0.45167 -0.54149 0.35751 -0.28531 -1.7811 0.36894 1.0425 -0.1766 -1.624 -1.0696 -0.038264 -0.43123 0.70437 0.16989 -0.040009 0.23095 -1.2516 -0.38634 -0.48899 -0.16254 0.42033 0.95423 -0.19121 0.58165 -0.352 0.070424 -cheered -0.36736 -0.75968 0.58122 -0.97144 0.67499 -1.3697 -1.0251 0.67159 -0.91611 0.57276 -0.20035 -1.1847 0.32108 -0.15279 0.12164 -0.43876 -0.44532 -0.13007 -0.4929 -1.6374 0.063092 0.79245 -0.40301 -0.68502 0.75523 -0.86984 0.0034651 0.94789 -1.0197 -0.67773 0.89835 1.155 0.78769 0.0033813 -0.96015 -0.92311 0.24456 -0.41321 -0.75342 -0.13604 -0.25122 0.40347 -0.68194 0.030855 0.77613 -0.46508 -0.9891 -0.34142 -0.79904 -0.86607 -ruined 0.82673 -0.20167 -0.10983 -0.25694 0.026495 -0.29472 -0.77607 0.71564 -0.26778 -0.086746 -0.49155 -0.29892 -0.42902 -0.60534 0.61081 0.35187 0.40647 0.1832 0.33923 0.41912 -0.50452 0.23954 -0.92762 -0.47327 0.46158 -0.2901 -0.70215 0.47425 1.2282 0.093908 1.4671 -0.23489 0.68258 0.21103 -0.37193 1.3406 -0.0048483 0.51056 0.45971 -0.14757 -0.59901 -0.50558 0.11323 0.1006 -0.19276 0.62828 -0.91158 -0.6909 -0.0071825 -1.0662 -handover 0.67345 0.042859 -0.5864 0.38332 0.6654 -1.0268 0.61094 0.37556 0.27215 -0.44165 -0.37476 -0.70879 0.043871 0.2888 0.56376 0.85689 -0.16391 -1.0137 0.40576 0.29306 1.2682 0.0077171 -0.64059 -0.76626 -0.018818 -0.30705 -0.074473 -0.28338 0.55233 0.83974 1.3808 0.089209 -1.6368 0.3986 0.25417 -0.1505 0.6876 -0.059074 -1.6321 0.29652 -1.0748 -0.076456 -0.26227 -0.54665 -0.81181 -0.93403 0.069974 -0.069813 -0.11894 -0.2668 -approves 0.17652 -0.52094 0.20237 -0.010022 -0.104 0.55525 0.44553 -0.55625 0.65834 -0.16132 -0.32826 -0.17223 1.0287 0.92112 0.40787 0.40425 -0.13678 -0.28024 0.80565 0.43909 0.89771 -0.70517 -1.171 -0.44491 -0.94997 -1.2981 0.93829 -0.34244 -1.128 -0.025655 1.3527 -0.017446 -1.6578 0.84294 0.19412 -0.20123 0.6935 -0.68013 -0.037621 -1.3448 -0.26892 0.22294 0.58054 -0.64221 -0.74756 0.91419 0.12358 1.4457 0.067607 0.41134 -tutsi 0.74322 -1.711 0.27562 -1.3226 0.66681 0.6662 -0.33421 -0.4434 -1.364 -0.13338 0.82581 -1.8121 -0.52633 -1.033 0.19333 -0.28667 0.45185 -0.078558 0.41881 0.48947 -1.561 0.77668 0.0077182 0.73897 -1.4701 -0.52793 -0.88573 -1.0572 -0.10993 1.4625 0.7512 0.77381 -2.1225 0.28725 0.079563 0.21433 -1.5484 -0.99761 -0.12159 0.60716 -0.45793 1.0368 -0.35613 -1.0319 -0.30838 0.13831 -1.0778 -0.13384 -0.641 -1.8483 -consult 0.80756 -0.2623 -0.24342 -0.27774 -0.04636 -0.70724 -0.19946 0.81805 0.059431 -0.7766 0.12002 0.86868 0.35512 0.49932 -0.12203 0.91699 -0.30097 -0.58876 1.5595 -0.4194 0.49682 0.36677 0.45461 0.76392 0.34441 -1.1763 -0.066105 -1.0392 -0.77561 -0.18147 1.2979 0.6574 -1.335 -0.56965 0.15307 -0.21291 -0.039941 0.39165 -0.0021824 0.057118 0.17866 -0.24308 0.43812 0.63294 0.48434 0.51859 -0.1409 1.3064 -0.22893 0.48179 -bust 0.35826 0.16594 0.3303 -0.50035 -0.080152 -0.24301 -0.28507 -0.77032 0.0075939 0.53214 -0.40092 0.37744 -0.55489 0.38128 1.1003 0.11828 0.08856 0.094046 -0.22868 0.033806 0.33011 -1.2788 -0.91618 -0.6697 -0.38468 -1.4268 -1.0676 0.35504 0.28525 -0.20562 0.79885 -0.27519 0.16428 0.15333 -0.31239 0.69789 -0.13174 0.40194 0.4135 -0.49146 -0.75942 0.26636 0.094823 0.32706 0.49861 -0.23081 0.33029 -0.9119 0.4611 -0.95862 -unlimited 0.10005 0.75968 0.54155 -0.21724 -0.22628 0.23566 -0.205 -0.32473 1.5807 0.95483 -0.24273 0.73613 1.0193 -0.037762 0.40001 -0.20493 0.68372 0.14364 -0.018556 -0.73481 0.75027 -0.69626 -0.14122 0.16016 -0.48564 -0.59729 0.3466 -0.49936 0.89607 0.3599 1.5809 0.74914 -0.8477 0.32235 -0.36374 0.0079085 -0.021231 -0.67088 -0.69102 -0.56245 0.92514 -0.067667 -0.31565 1.2512 -1.3363 -0.73839 -0.34324 0.55433 -0.49443 0.40281 -explicitly 0.0058917 -0.28056 -0.27778 -0.6508 0.27178 1.0285 0.2553 -0.85981 -0.37551 0.13433 0.13805 0.61588 0.23752 -0.26036 0.42846 0.43674 -0.0097834 -0.77966 1.0319 -0.073813 0.059645 -0.56768 0.26104 0.43702 0.22409 -1.4827 -0.077118 -0.16341 0.0029711 -0.605 1.3377 -0.76727 -1.1228 -1.1741 -0.32714 -0.78705 0.79134 -0.2174 -1.9406 0.7293 0.3657 -0.35725 0.15083 0.60294 -0.56441 -0.41584 -0.48569 0.89244 0.0030604 0.13092 -premiums 0.16995 0.08008 1.0474 -1.6206 -0.54574 1.4235 -0.37881 -0.35621 0.86092 -0.11447 -0.098863 -0.0085985 0.96422 -0.53572 0.61617 -0.071883 -0.42365 -0.49184 0.32685 -0.8086 0.72924 -1.1018 -0.52331 -0.38441 -0.79685 -0.9642 -0.417 -0.44202 0.12787 0.59452 1.6396 1.3807 0.72283 0.6149 0.11557 -0.98592 -0.18016 -0.2558 1.1175 -0.8731 -0.86465 -0.16582 1.1358 1.2289 -0.71698 -1.7236 -0.039149 0.60408 0.56985 1.0654 -aston 0.60542 0.77248 0.08448 0.048732 -0.39726 0.0077239 -1.1005 0.096143 -1.2651 0.19704 -0.31734 0.63738 -1.4987 -1.7924 0.084514 0.03171 0.71598 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-fatalities 0.54952 -0.16885 1.033 0.19739 -0.55965 1.0261 0.19506 0.14027 1.3386 -0.84187 0.090653 -1.3063 -0.095521 -0.68996 1.0619 -0.94211 -0.58287 0.098243 -1.0914 -0.39861 -0.74333 -0.30774 0.28489 -0.24737 0.50273 -0.24336 -0.68502 -0.38306 0.57612 1.1937 1.5656 0.2888 0.97821 -0.29582 0.78702 -0.044919 0.79022 -1.0954 -0.25 1.2869 -1.684 -0.030812 1.1136 -0.14964 0.57517 -0.63199 -0.55029 0.66539 0.58971 0.013055 -550 0.98141 -0.1524 1.3669 -0.37813 0.098665 -0.62303 0.67371 -0.5995 0.027437 -0.40293 -0.87347 -1.1812 0.51615 -1.0086 0.57259 -0.38064 -0.12093 1.3157 -1.0006 -0.24327 0.56399 -0.19213 0.29022 -0.026183 -0.34165 -0.18111 0.16457 -0.31613 -0.65957 0.019042 1.4936 0.020833 0.41906 1.2594 0.53635 -0.24466 0.25015 -0.14709 0.0075318 0.21409 0.2158 0.021239 1.2694 0.16217 -0.75492 0.43545 -0.07933 0.52407 -0.95021 -0.085549 -petrol -0.19668 -1.1128 1.3836 -1.0902 -0.36215 0.22415 -0.3974 -0.74339 -0.10337 0.76858 0.30739 -0.54309 0.35393 -0.4402 0.34935 0.32305 -0.097857 1.6659 -0.66867 -1.6461 1.2319 -0.64555 -0.11715 0.37206 -0.40468 -0.39696 1.0123 1.2202 0.7806 1.3668 1.5918 0.35278 0.216 1.0269 0.70292 0.67159 0.32321 -0.92054 0.59745 0.93403 0.55596 0.26355 -0.62368 -0.22405 0.67364 0.50568 -0.85685 0.95046 0.95547 -1.0639 -pneumonia 1.9092 -0.030739 -0.38855 -0.02833 -0.81407 1.4426 0.54126 0.39861 0.93436 0.2453 0.37866 -0.036323 0.35323 0.30728 0.80046 0.011978 -2.3468 0.017204 -0.89486 1.4422 -0.64237 0.25329 1.2835 -0.052196 0.689 -1.1068 0.35827 -0.063975 0.39294 1.139 1.2084 0.33195 0.84418 -0.56006 -0.041352 -0.0079726 0.74421 0.41369 0.91006 0.32731 -1.368 0.085717 -0.078643 0.64804 1.7885 0.83245 -0.30056 -0.81974 0.6957 0.58728 -customary -0.31728 0.15777 -1.675 -0.20692 0.7025 -0.24697 0.25225 0.27492 -0.096053 -0.58371 0.072804 -0.13537 0.69882 -0.16811 0.094023 0.092362 -1.0211 -0.4608 0.071887 -0.69354 0.21642 0.62024 -0.4785 -0.48003 -0.10249 -0.63188 -0.2335 0.41348 0.19799 0.4976 1.645 0.45627 -0.37092 0.43926 0.30827 0.081524 0.51737 -0.21975 -0.96194 0.52084 0.31068 0.26405 -0.049454 1.0071 -0.32081 -0.87556 -0.087451 0.89897 0.062651 -0.22387 -h1n1 1.1841 -0.88567 0.27751 -0.30873 -0.2766 0.26182 0.39518 -0.77824 2.1509 0.23481 0.87719 -1.0442 1.0739 -0.29693 1.3843 -0.28337 -1.8033 -0.63136 -0.94989 1.602 -1.5762 -0.72746 1.1545 0.5021 0.75741 -0.87275 0.52281 -0.33949 -0.70837 0.49071 1.5782 0.31592 0.44677 -0.34336 -0.20043 -0.45633 1.0943 -0.28848 -0.082099 -0.12744 -0.74457 0.043327 0.83689 -0.34624 1.4672 1.0751 0.37872 0.90209 0.93472 0.34263 -immense 0.57475 1.0047 -0.075977 -0.10265 0.68588 0.089979 0.94701 0.51634 0.64101 0.58185 -0.0050519 0.25553 0.019801 -0.77634 -0.19861 -0.21293 0.81601 0.41886 -0.023622 -0.78178 -0.052265 -0.16631 -0.82109 -1.4058 0.67113 -0.60681 -1.0773 0.27675 0.65471 0.92293 1.7111 0.92753 0.841 -0.69687 -0.82447 0.90559 -0.79475 0.026581 0.037605 -0.1969 -0.020701 0.17886 -0.43704 0.25249 -0.60114 0.0084353 -0.37088 0.1179 -0.36812 -0.99933 -downing 0.47192 0.24456 0.031919 0.21157 0.15886 8.5881e-05 -0.7526 0.43651 -0.12073 -0.62667 -0.50089 -0.091434 -0.70815 0.15536 0.035713 -0.41295 -0.6609 -0.48112 -0.69113 -0.16083 0.16549 0.74468 -0.1737 -0.20419 0.1222 -0.83691 0.84383 0.38613 -0.1106 0.17501 0.46111 -0.22432 -0.026159 0.13411 0.30612 -0.050402 0.55461 0.14908 -0.28807 0.16378 0.79326 0.31562 0.28033 -0.63236 0.81823 0.98619 -0.61654 0.30745 0.27519 -0.24164 -illicit 0.65862 -0.31589 -0.37524 0.27235 -0.1308 0.31209 0.33723 -0.87769 1.7807 1.4057 0.031031 0.56546 -0.083261 -0.74493 0.77859 -0.47908 0.14634 0.33403 0.33597 0.31381 1.5352 -0.32597 0.95431 -0.32024 -1.136 -1.36 0.83822 -0.07371 1.388 0.092886 1.412 -0.037292 -0.3679 -0.46643 -0.16454 0.54817 -1.5591 0.17596 -1.0283 0.32518 -0.044731 -0.19615 0.75553 0.38712 0.70944 -1.4158 -0.22321 0.55814 0.098125 -0.9106 -clinch -0.16573 0.38769 0.35682 1.1672 0.28741 -0.3608 -0.45591 1.6107 0.16262 -0.080217 -0.39898 -0.48275 -0.64195 0.17652 -0.26622 -0.10937 1.1445 -1.0284 -0.82017 -0.30982 -0.61976 -0.90277 -0.15842 -0.17468 -0.066194 -0.49764 0.28468 -0.61367 0.13955 -0.81722 1.2991 1.0775 -0.82958 0.69572 0.66753 -0.20754 -0.15195 0.9921 -0.4986 -1.3101 -0.093881 -0.88434 -0.10642 -0.073994 -0.50739 0.75471 0.24484 0.092469 0.10111 -0.77392 -discontinued -0.011811 -0.50598 -0.17364 -0.1271 -1.5994 0.37229 -1.0421 -0.71731 0.24712 -0.18269 0.95169 0.43201 -0.073376 -0.80095 0.54759 0.34378 -1.8241 0.58171 -0.44412 -0.16994 0.60502 -0.83272 0.13291 -0.074552 -0.53586 -0.20887 -0.28658 0.052119 0.42103 0.44202 1.5006 0.012307 -0.073629 -0.30084 0.26721 -0.17501 0.54113 0.30078 -0.81856 -0.34316 -0.16627 -0.73535 -0.39607 -0.20084 -1.07 -0.036017 -0.11099 -0.23674 -0.20033 0.37054 -1878 -0.52158 0.011684 -0.89484 -1.0223 -0.33362 0.63919 -0.6415 0.044326 -1.1168 -1.1656 0.25779 -0.010435 0.15325 -0.80168 0.13234 -0.35127 -0.74018 0.21104 -1.1088 0.76851 1.3057 -0.5551 0.249 -0.11684 -0.0014521 -0.32763 0.29808 -0.57804 0.043193 0.65989 0.72788 -1.0422 -0.60329 -0.48814 -0.055729 -0.26859 0.58249 0.66772 0.39237 0.495 0.029226 -0.89026 -0.0025051 -0.62309 -1.0198 0.54453 -0.46332 -1.1769 0.34924 -0.21432 -shortstop -1.3954 0.51089 0.20798 0.94811 0.90473 -0.74252 -1.6431 0.6951 -0.42733 0.72189 0.8942 1.3909 -1.232 1.0553 0.50541 -0.91045 0.15558 -0.53617 -0.35192 0.22878 -1.4898 -0.80352 -0.29882 -0.05893 -0.24365 -0.65373 1.2587 0.14921 1.1643 -1.384 1.0134 -0.084485 0.84332 0.53795 0.48744 0.31974 -0.15648 0.83837 0.2685 0.82967 -0.65016 0.77806 -0.49319 0.07469 -0.8853 -0.7574 0.23234 0.23769 -0.6498 0.48721 -pitt 0.26494 0.30876 -0.16624 -0.08208 -0.19346 0.53783 -0.82298 -0.26683 -0.83569 -0.44675 -0.82974 0.9068 -0.44483 -0.15759 0.048443 0.20511 0.64618 -0.026077 -0.60905 0.46012 -0.030049 0.50303 0.25645 -0.1055 -0.10555 -0.89995 0.11488 0.1844 -0.8047 -1.0998 0.03911 0.23612 0.364 -0.8038 0.53977 -0.22141 -0.53362 -0.30027 0.16367 -1.2205 -0.54366 0.45093 0.10084 -0.58844 -0.43178 -0.014817 0.12928 -0.35769 0.29147 0.70469 -sings -0.39161 0.69258 -1.0484 -0.64868 0.17466 0.69535 -0.19553 0.50568 -0.34699 1.1631 -0.024079 0.60423 0.25174 0.11967 0.23758 -0.46206 -0.014198 -0.14495 0.48268 -0.45587 -0.4943 1.0756 0.2766 0.72613 1.4678 -0.14368 -0.64529 0.3796 -0.29807 -1.2216 1.4616 -0.26673 0.7112 0.83071 -0.06346 -0.30583 0.74392 -0.79103 -0.29788 -0.19452 0.37028 0.53698 -0.94166 -1.128 0.24508 0.14314 0.31707 -1.4277 -1.219 0.65133 -amr 0.89911 0.97321 0.31352 0.76334 0.82812 -1.2781 -0.54542 0.39029 -0.46357 0.13216 0.43004 0.97933 -0.73076 0.72689 -1.3087 0.70804 -0.27861 -0.81445 -0.030888 1.3622 0.082733 0.55446 0.66121 -0.73006 -0.59121 -0.26653 0.22886 -0.19014 -0.50297 0.69502 1.2722 0.32489 0.28394 2.1498 0.82597 -1.6304 -0.15576 -0.27442 0.4623 1.4145 0.20645 -0.066372 -0.24254 0.080539 0.29261 -0.29463 -0.91186 1.7164 -0.12713 0.50057 -acknowledging 0.27848 -0.12609 -0.31425 -0.36194 0.23798 0.54119 0.22517 0.46549 -0.049198 0.051451 -0.1354 0.1481 -0.57905 -0.78378 0.46863 -0.064625 -0.54635 -0.43002 0.82709 -0.058369 -0.099328 0.22934 0.04198 -0.70166 0.68548 -1.3448 -0.12247 0.39083 0.1424 0.045446 1.0592 0.48598 0.22144 -0.91285 -0.54995 -0.38114 -0.16534 -0.021765 -0.85956 -0.12624 -0.24821 -0.27982 -0.24667 0.11083 -0.20157 -0.24554 -0.42675 1.3688 -0.081502 -0.21477 -toilet -0.08036 0.14095 0.3271 -0.6213 -0.26604 -0.17687 0.56078 -0.37128 0.66286 -0.31873 -0.51579 -0.083818 0.47204 0.66723 0.1783 0.7449 -0.51092 1.3322 0.16289 -0.42128 0.95649 0.55281 0.26533 0.045567 0.056375 -1.012 -0.31628 1.0045 0.85523 0.010025 1.2113 0.35284 -0.34378 0.51575 -0.55059 1.7334 1.1273 0.7689 0.80078 -0.35173 0.99743 -0.24538 -0.13003 1.5894 0.61017 0.64248 -0.27321 -0.58731 0.59987 -0.4758 -simmons -1.1595 0.1544 0.21369 0.44027 -0.043795 0.40457 -0.62703 -0.26942 -0.15745 0.35529 -0.34459 1.0121 -0.95492 0.010569 0.0011838 0.33816 -0.019378 -0.09267 -0.019328 0.36571 0.17154 1.074 0.64169 -0.055234 -0.21613 -0.64538 0.25187 -0.43413 -0.18104 -0.48931 0.64472 -0.37149 0.52138 0.065245 0.79091 -0.042287 0.37093 -0.027813 0.65127 0.051755 -0.011648 0.61219 -0.57896 0.23181 0.17075 0.37489 -0.24 -0.55731 -0.2924 0.89118 -knocking -0.40224 -0.84326 0.90388 0.17896 0.027776 -0.13575 -0.063822 0.87957 -0.13694 -0.052447 -0.30392 -0.40072 -0.84384 0.43393 0.12592 -0.08954 0.054113 -0.065761 -0.4791 -0.85148 -0.16963 0.22704 0.030008 -0.077332 0.56129 -0.79455 0.31103 0.3549 0.86384 -0.71758 1.3614 0.55329 0.28731 0.20882 -0.45006 0.57992 0.1589 0.31659 0.25377 -0.20736 -0.030928 0.16999 -0.17455 0.26189 0.093694 0.39589 -0.079826 -1.2141 0.06985 -0.69042 -scoreless -0.43897 -0.12396 -1.0549 0.66517 0.527 0.2064 -0.73402 1.5978 -0.93362 0.24274 -0.93033 -0.48617 -0.87219 0.29205 0.22887 -1.9353 0.37036 -1.4601 -1.1642 0.58136 -1.0158 -0.4013 0.10269 -0.81653 -0.45066 0.59812 2.0762 -0.29067 0.76221 -0.95514 2.0112 1.0672 0.60074 1.2702 -0.053982 0.82112 0.21198 0.80688 0.20091 -0.29041 -0.62993 -0.77165 -0.60549 0.22169 -0.20372 0.64076 0.30333 -0.021648 -0.93068 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0.84617 0.09336 -0.038989 -0.37023 0.095734 0.62357 -0.63061 -0.37312 -0.1177 -0.47815 -0.26302 -0.52944 -gerry 0.26963 0.39584 -0.42541 -0.21852 0.035365 0.80029 -0.91684 0.65582 -0.59375 -0.7559 0.065825 0.068659 -1.3616 -0.25672 0.84221 0.43208 0.8796 -0.41453 0.014242 -0.2731 -0.29749 0.37332 0.14883 0.89089 -0.50659 -0.30825 -0.072035 -0.27931 -0.93899 0.51796 0.043947 0.27201 -0.66278 0.42842 -0.18161 -0.6484 0.10641 -0.378 0.96205 0.38193 0.58087 0.87605 -1.3953 -0.52238 0.32186 0.18727 -0.4349 0.677 -1.042 0.79084 -jupiter 2.2431 1.3593 1.0027 0.74489 0.38023 -0.49881 -0.43995 -0.35627 -0.1761 0.62409 0.74536 0.50709 0.21883 0.90979 -0.50444 0.63401 -0.39978 0.28323 -2.1442 0.27225 -0.4343 -0.1694 0.29609 -0.38939 0.24235 -0.31603 -0.39842 0.36277 -0.24008 -0.23794 0.69893 -0.17229 0.13965 -0.74697 -0.45963 -1.0565 -0.63789 -0.41344 0.74397 0.16217 0.14665 -0.29287 -0.27353 0.70475 -0.22558 -0.36284 0.96752 -0.57552 -0.57269 0.071148 -sack -0.95494 -0.66208 0.1562 -0.49587 0.62086 0.071093 -0.020749 0.39319 -0.90923 -0.50453 -0.60734 0.60346 -0.27765 -0.11381 0.78074 -0.12536 0.083543 -0.2445 0.071528 -0.075851 -0.20081 -0.62759 0.28176 -0.46693 -0.52252 -0.79109 0.50976 0.10948 0.49985 -0.00037475 1.1042 0.47588 -0.57232 1.0013 0.39774 1.6163 0.48662 0.22755 0.61263 0.0021158 -0.049347 0.96476 -0.94489 0.18962 -0.4115 0.53086 -0.20119 0.47216 0.35903 -0.53089 -dismantled 0.91761 -1.4536 0.90976 -0.32676 -0.61259 -0.39117 -0.6634 -0.17417 -0.14209 -0.43796 0.56488 -0.52198 -0.98423 0.17688 0.14387 0.94043 0.21275 0.30547 0.34414 0.75991 0.29374 -0.6141 -0.29078 -0.36387 -0.78158 -0.75859 0.36918 0.35712 0.82602 -0.24838 1.2139 -1.4857 -0.45578 -0.51651 -0.34667 0.80367 0.87573 0.76887 -0.70207 0.43513 -0.15357 -1.2283 0.6917 0.096612 0.064769 0.072255 -0.88259 -0.46064 -0.84623 -1.1482 -hollow 0.082464 0.65117 0.00021402 0.05892 0.35203 0.96091 0.32064 0.12722 -0.20135 -0.19886 -0.94365 -0.28389 0.18801 0.23343 -0.8376 0.73969 1.032 0.75095 -0.4488 -0.55039 -0.32996 -0.15042 0.1016 -0.20191 0.46763 -0.71116 -0.50483 1.602 0.5628 -0.99045 1.1823 -0.94835 0.29643 0.32703 -0.045053 0.045513 0.49355 -0.264 0.086842 -0.57161 -0.054654 -0.6427 -0.77484 0.50653 0.41206 0.29751 0.20313 -0.54766 -0.35288 -0.48758 -resting 0.6403 1.0128 -0.1428 -0.62449 1.1752 -0.24643 0.014539 0.72341 0.36051 -0.60039 0.25079 -0.13347 0.050561 0.54013 -0.10974 0.75595 -0.10306 -0.28665 -0.15758 -0.35931 -0.83181 0.13356 0.157 -0.21599 0.14475 -0.26257 0.36226 1.0718 0.34183 0.20104 1.5021 0.4467 0.46414 0.083328 0.28903 0.60876 0.68308 0.71023 0.28219 -0.13391 -0.26695 -0.42484 -0.62209 0.93482 0.2569 -0.31369 0.23583 -0.84968 -0.10813 -0.75126 -speculative 0.20012 -0.81532 -0.16283 -0.89016 -0.45842 -0.54501 0.12212 -0.77185 -0.73675 1.7957 -0.61533 1.0649 -0.27612 0.019636 -0.11724 0.17291 0.93188 -0.30541 -0.94383 0.16129 1.542 -0.609 -0.091763 -0.44958 0.0074641 -0.77712 -0.13139 0.2596 0.63565 1.0024 1.9064 -0.60125 1.2229 -0.32862 -0.32456 -0.0057504 -0.99609 -0.57385 -0.42807 -0.3684 -0.34404 -0.25273 0.069457 1.1772 0.68357 -0.90035 0.45877 1.0353 1.1124 -0.55101 -bowls -1.9375 0.3471 -0.88034 0.62694 0.2484 -0.12415 -0.30455 0.17432 -0.71742 -0.29555 -0.21123 0.32036 0.82836 0.90034 0.30951 0.1316 0.016471 0.92679 -0.82302 -1.0926 -0.14515 -1.0152 1.1164 0.068663 -0.38988 -0.060218 0.1603 0.1397 0.59576 -0.088601 1.392 0.81025 -0.013155 1.1087 0.57328 1.4687 -0.11376 1.7417 0.14805 -0.10186 -0.29465 0.30479 -0.81242 1.5553 0.01989 1.7263 -0.39542 -0.46469 -0.59616 -0.98218 -wipe 0.51528 -1.1413 0.86766 -0.51078 -0.17499 0.02666 0.65242 -0.16563 0.47193 0.34104 -0.312 -0.23671 0.027793 -0.15905 -0.0017464 0.76454 0.54033 -0.46575 0.39253 0.40558 -0.32555 -0.19096 0.76034 -0.74325 -0.39515 -0.7889 -0.35909 0.25972 1.2871 -0.49672 1.663 0.58516 -1.2857 0.66727 -1.2997 0.74565 -0.63313 -0.38339 0.67286 -1.0193 -0.079047 -0.33748 0.11034 0.060682 0.43673 -0.039153 0.2811 -0.082868 0.17995 -0.789 -hindi -0.8455 -0.573 -0.61052 -0.46028 -0.36987 -0.066955 0.54013 -1.4048 -0.66331 1.174 0.32141 0.34008 0.61584 0.60467 0.12372 -0.40095 -0.01299 1.415 0.016147 1.875 0.6651 0.55634 1.4182 1.4715 0.9005 -0.75903 -1.2554 -0.40647 -1.3368 -0.12038 1.853 -0.42007 0.38702 0.75926 0.4177 0.59443 0.1108 -0.45685 -1.2849 0.68669 -0.082963 1.2654 -0.61022 -0.8162 -0.70583 -0.37655 -0.010652 0.50118 0.35677 -0.14428 -countered -0.24579 -0.9287 0.32655 0.22558 0.0002015 0.32242 -0.27863 0.3459 -0.48015 0.20635 -0.51085 0.03878 -0.52519 -0.4578 -0.2256 -0.14283 0.32656 -0.91753 0.26056 -0.63847 -0.17106 0.090257 -0.02415 -0.83245 0.10536 -1.2141 0.29601 0.14069 -0.17246 -0.48757 0.73028 -0.036332 -0.0002023 -0.39826 -0.52905 -0.91555 -0.1493 -0.031516 -0.25212 -0.074165 0.36124 0.45312 -0.12208 0.15353 0.2556 -0.28191 -0.63302 1.0383 -0.17331 0.40512 -abbott -0.20561 -0.74475 -0.13819 0.55814 0.074791 1.0136 -0.89952 -0.42459 0.074017 -0.019922 0.010904 0.31271 -0.80082 -0.069384 -0.0088145 0.40649 0.14492 -0.2571 -0.21467 -0.056355 0.12668 0.41549 -0.010971 -0.44971 -0.29269 -0.32134 0.2105 -0.52713 -0.59237 -0.32988 0.50037 -0.29435 0.33441 0.053782 0.51056 -0.49628 0.38929 0.50983 0.75058 -0.14287 -0.1181 0.18716 -0.32247 -0.11152 0.50885 0.59658 -0.55586 -0.13257 -0.081398 1.1291 -distances 0.7299 1.0879 0.79166 -0.25898 -0.13408 0.16394 0.4896 -0.012172 0.51369 -0.20944 0.40706 -0.50197 0.23784 -0.09581 -0.066298 0.36124 0.83396 0.14953 -1.1044 -1.3326 -0.22009 -0.58397 0.47271 0.4813 1.324 -0.15255 0.058505 0.036253 0.759 0.099916 1.7357 0.83537 0.22902 -0.61028 0.13059 -0.2214 -0.20662 0.96119 -1.2746 1.1198 0.25735 0.068011 0.67189 1.1368 -0.96215 -1.0863 0.99481 -0.29391 -0.55145 -0.47793 -gubernatorial -0.61693 -0.12947 0.37751 1.1643 -0.70889 1.1498 -0.92813 0.27799 -0.52824 -1.068 -1.5572 -1.0372 0.32266 -0.13523 0.35409 -0.62212 0.10165 -0.17419 -0.2744 -0.27228 -0.0091034 -1.0183 0.50282 -0.10812 -0.11592 -1.2902 -0.12893 -0.45771 -0.66851 0.65168 0.41446 0.62736 -1.4266 -1.3 0.22926 -0.31016 -1.1069 1.0149 -0.78887 -1.5457 -1.067 0.77684 -1.4435 -0.19115 -1.7196 -0.23868 -0.53021 -0.25284 0.79777 0.64102 -revered 0.1408 0.66065 -0.24684 -0.73193 1.6113 -0.8066 -0.17967 -0.26196 -0.22112 0.93986 -0.042221 -0.54583 0.38497 0.21451 0.18314 0.58812 -0.016538 0.16283 -0.026054 0.95324 -0.92085 0.22123 -0.75864 -0.049945 0.19538 -1.3231 -1.5615 -0.38143 -0.82512 0.706 0.58092 -0.32089 0.35303 -0.13057 0.29535 0.19412 0.15426 -0.012475 -1.3252 0.42299 -0.70463 0.64417 -0.79717 0.72704 0.50564 0.83929 -1.4556 -0.79072 -0.51482 -1.454 -eruption 1.6635 0.40628 0.47934 -0.82129 -1.0248 0.082773 -0.046716 0.59721 -0.5126 0.5029 0.27224 -1.0477 0.5289 0.30646 0.85317 0.60718 0.42904 0.20092 -2.3061 0.97897 -0.74013 -1.0866 0.39348 -0.44759 0.26158 -0.14448 -0.61407 0.79267 0.36925 1.4682 0.97502 -1.087 -0.13877 -0.81178 -0.61932 0.021197 0.15359 -1.8475 0.80918 0.63998 -1.0351 -0.02929 -0.74769 -0.31271 0.11182 0.25391 0.5418 0.59016 -0.55545 -1.1122 -oecd 0.14376 0.07637 -0.10996 -0.96256 0.49754 -0.4888 0.10178 -1.3472 0.6287 -0.65697 0.93322 -0.41681 0.12748 0.77284 0.55394 0.01566 0.58087 -0.96183 0.65632 -0.032388 -0.24112 -1.2087 0.38183 0.0052999 -0.35482 0.35335 0.56969 0.48691 -1.7475 1.1706 1.557 0.23433 0.47528 -0.22639 -0.92365 -1.2374 -0.18594 0.10545 0.14193 -0.18941 -0.99187 -0.64706 1.2214 -1.3531 0.44419 -0.26075 0.17071 1.306 0.69148 0.34523 -samoa -0.43301 -1.024 -0.93102 0.9616 -0.15194 0.1724 -0.1601 0.8564 -0.28863 -0.71569 0.70684 -0.34279 1.0157 0.18237 -0.128 0.25402 0.5632 -0.19455 -1.561 0.43735 -0.64774 0.4717 -0.18453 0.39104 0.65373 0.085097 0.60989 -0.64659 -0.76344 -0.33831 0.66722 1.2382 0.044352 -0.08091 0.078743 0.098809 0.11556 -0.74551 0.0031787 -0.65341 -0.58804 0.19187 0.81089 -0.99407 -0.91343 1.8874 -0.46207 0.077347 0.63476 -0.43695 -2-3 -0.079664 0.49766 0.29363 0.92932 0.67661 0.40584 -0.13695 0.69599 -0.49422 -0.39123 -1.1389 -0.64486 0.06127 -0.030631 0.60769 -0.7362 -0.17188 -1.3627 -0.78377 0.92827 -0.47427 -0.57691 1.322 -0.42235 0.026052 1.2364 2.1768 -0.52952 1.1109 -0.69568 1.7156 0.73461 0.15298 0.75951 0.042846 0.20548 0.19432 -0.11295 0.48105 -0.094493 -0.79387 -0.68663 -0.35392 -0.41209 -0.074337 0.58423 0.90171 -0.046876 -0.59963 0.72317 -earnhardt -0.066198 0.39284 0.86873 0.91644 0.24217 0.4714 -1.4248 0.32206 0.19271 -0.88206 -0.47464 0.19572 -2.2784 -1.1798 1.1139 -0.79989 -1.1196 -0.0057659 -0.37636 -2.2749 -1.4079 0.4243 -0.46696 -0.11972 1.009 -0.39061 -0.64476 -0.44239 0.51851 0.022362 -0.16343 0.010552 -0.57342 0.10021 0.60242 -0.64006 0.32804 0.28338 0.25913 0.012904 -0.41394 -0.32135 -0.89803 0.55225 0.085024 0.23352 -0.21622 -1.2426 0.36097 -0.26293 -everett -0.011615 0.27628 0.19661 -0.20249 0.69122 0.48552 -1.4743 0.031559 0.15142 -0.34906 -0.27391 0.67186 -0.58786 0.033457 0.33835 -0.194 -0.123 0.0056192 -0.75598 0.039432 -0.11873 -0.43656 -0.68351 0.05856 0.30691 -0.94416 0.52563 0.14385 0.66637 -0.48927 -0.15361 -0.57591 0.61992 -0.33836 0.1931 -0.47203 0.28264 0.36851 0.54773 0.93323 -0.095255 0.77257 -0.69262 0.050076 -0.48401 0.78904 0.06452 -0.15594 -1.2438 1.1408 -marxist -0.44724 -0.74367 0.24949 -0.64595 -0.19391 -0.15128 0.42934 -1.4647 -1.3412 0.537 0.41039 -0.44089 -1.0733 0.37012 -1.2235 -0.92937 0.027789 0.58918 0.60275 0.93366 -1.3025 0.62346 0.28603 0.16446 0.28092 -1.1113 -0.45251 -1.0507 -1.0546 1.3253 1.5757 -1.7787 -1.6082 0.00058073 -0.65792 -0.67561 -1.6044 0.26581 -0.17785 0.5905 0.056832 -0.37825 0.11158 0.47502 -0.036524 0.087114 0.28862 0.0089039 -0.10137 -1.5283 -bologna 1.1444 -0.81117 -1.4844 -0.4381 0.074385 -0.61125 0.025821 -0.33074 -1.4416 0.54004 0.12569 -0.62678 -0.17099 -0.67438 0.062094 -1.4678 -0.6109 0.25955 -0.89278 0.95611 -0.46918 -0.42878 -0.24556 0.76318 -0.44466 -0.42965 1.1653 0.44225 -0.65406 -0.76654 1.0743 0.93283 -0.16853 0.12356 -0.30396 0.39219 0.2029 1.8895 0.95429 -0.13466 0.77791 -0.0038449 0.20357 -0.62053 0.045134 1.4153 0.4148 -0.055174 1.0996 -0.29872 -weber -0.18437 0.68471 -0.1309 -0.074887 -0.28621 0.66118 -0.40165 0.23367 -0.88344 -0.47254 0.16788 -0.034194 -0.78176 -0.025839 0.65391 0.040329 0.70015 0.0063519 -0.4044 -0.44052 0.038248 -0.61062 -0.15054 0.45046 0.16615 -0.4226 -0.11901 0.055615 -0.81161 0.075777 0.43054 -0.4211 0.18709 -0.28931 -0.62393 -0.70477 0.15851 0.80898 1.3092 0.79157 0.52085 0.61366 -0.44402 -0.42376 0.23058 0.12785 0.22098 0.078312 -0.60925 1.1225 -volvo 0.63299 0.058958 0.34299 0.46965 -0.5759 0.30802 -0.87815 -0.64286 0.55273 -0.56711 0.97612 -0.17921 -1.4509 -0.4461 0.48595 0.87566 -0.38775 1.4152 -0.49003 -1.2947 0.24412 -0.1689 -1.2224 0.24498 0.34012 -0.35518 -1.2344 0.071918 -0.41256 0.3149 0.51976 0.89031 -0.010617 2.258 1.0976 -1.0593 0.33771 0.87739 -0.71844 -0.12717 0.19767 -0.94673 0.77958 -0.32512 -0.35139 0.49187 -0.10716 0.68552 -0.20319 -0.35038 -2000s -0.38104 -0.28187 -0.052712 -0.47931 -1.7672 -0.012005 -1.2861 -0.46926 -0.59528 0.57374 0.85334 -0.25902 -0.55535 0.059488 0.36801 0.19657 -0.20221 0.30718 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0.45278 0.04007 -0.43469 0.84691 0.77227 0.45823 0.2109 -0.35657 0.060676 -0.47388 0.37899 1.0398 -0.14798 0.18558 -wildly 0.12464 -1.4263 -0.099215 -0.84469 0.18443 -0.35458 -0.41054 -0.46783 -0.95352 1.202 -0.31725 -0.67352 -0.44074 0.13129 0.044 -0.5243 0.034694 0.079747 -0.76724 -1.4092 0.023809 0.31 0.25566 0.071781 0.87697 -0.47915 -0.43481 1.0437 0.019796 -0.13642 1.5436 0.40222 1.2487 -0.25525 -0.21417 -0.40183 -0.5307 0.56952 -1.6267 0.013751 -0.35422 0.52551 -0.6782 0.67407 0.18839 -0.977 0.19905 0.1215 -0.050529 -0.26623 -impacts 1.0361 0.17637 0.13156 0.16093 -0.6179 0.85738 0.45318 -0.4231 1.0176 0.048515 0.20577 0.22725 0.27507 0.29534 -0.21842 0.54647 0.98917 -0.43833 -0.47549 -0.78296 -0.17396 -0.47137 0.17608 -1.1159 1.0522 0.3959 -0.73881 0.28514 0.73229 0.63676 1.8495 0.43995 0.85633 -1.8188 -0.54938 -0.010773 -0.24729 -0.55465 0.15019 0.48548 -1.4764 -0.41758 0.72992 -0.0074726 -0.010864 -0.11666 0.37221 1.2002 0.67107 0.14235 -perot -0.034085 -0.40075 1.4634 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0.093243 -0.57257 0.76553 -0.483 -0.81369 -0.1468 0.060582 -0.80849 -1.2471 -0.78265 -0.13018 0.11778 0.86538 0.81829 -0.81556 -1.2913 0.29528 0.087123 -0.33271 0.19063 -0.4746 0.14118 -0.71324 -0.22985 0.65315 0.40909 0.39826 -0.17698 -0.072605 1.065 0.065097 0.6732 -swimmer -1.9629 1.1863 -0.11088 0.75005 0.0094412 0.50987 -0.28866 0.553 0.88822 -0.29245 1.6292 -0.23917 0.095285 0.27004 1.4715 -0.31836 0.95794 0.76406 -1.3376 -0.46941 -0.76416 0.50898 0.21003 0.21234 1.2091 -0.85 0.68819 -0.69845 -0.81222 0.63353 0.37064 -0.053242 -0.13084 0.21861 -0.097213 -0.051284 0.27187 1.1027 -0.39803 -0.70633 0.20033 0.06528 0.7418 -0.22192 0.689 -1.4853 0.2247 -1.1283 -0.073632 -0.19008 -correspondence 0.15274 1.1753 -0.48307 -0.27754 0.087814 0.16606 -0.31416 0.29855 -0.22761 -0.58778 -0.35329 0.94666 -0.061786 -0.52231 0.40133 -0.95604 -1.1802 -0.58628 0.74449 -0.085345 0.91719 0.048256 0.6775 -0.01213 0.39089 -0.60105 -0.2755 -0.50475 -0.4008 0.080041 1.4157 -0.92458 -0.093978 -0.97811 -0.10793 0.54881 -0.29457 0.67851 -0.38398 0.42485 1.494 -0.29323 -0.28868 1.0244 -0.37136 -0.68494 0.64899 0.69307 -0.67771 -0.65591 -totals -0.44604 0.57762 1.197 0.43244 -0.050386 -0.33809 0.21922 0.12019 0.5194 0.085832 -1.0851 -1.1942 0.55286 -1.0115 0.9568 -0.62813 -0.19742 -1.2647 -0.12955 -0.037304 -0.2449 -1.178 1.629 -0.61649 -0.49576 0.78104 -0.2275 -0.19444 0.21713 -0.29542 1.415 0.63813 0.21576 -0.053099 0.6777 -0.31045 0.41758 0.58255 -0.20585 -0.66274 -0.76962 -0.54693 0.33309 -0.16057 -1.2266 -0.0043904 0.16057 0.79344 0.16013 0.30825 -260 0.95635 -0.20746 1.3974 -0.26119 0.20464 -0.3781 0.61282 -0.49869 0.30583 -0.17838 -1.0179 -1.2659 0.53326 -0.60009 0.12179 -0.98025 -0.12741 1.0883 -1.0439 0.005886 -0.14232 0.0080947 0.57893 -0.22834 -0.18103 0.033337 0.47371 -0.20299 -0.65568 -0.062566 1.5052 0.17234 0.73645 1.06 0.63379 -0.28766 0.36999 -0.037013 0.15045 0.44895 -0.24937 0.049771 1.4279 0.21574 -0.93484 0.63878 -0.37417 0.27254 -0.3639 -0.10947 -nottingham -0.49569 0.48328 -0.61857 -0.083506 -1.0734 -0.15213 -0.48759 0.67417 -0.24306 -0.39498 -0.26414 -0.10022 -0.58036 -1.6818 -0.48369 0.25224 0.4847 0.28321 -1.2074 0.61588 0.53604 0.057706 -0.072637 0.88019 -0.75342 -0.69975 0.92025 -0.0054619 -0.58259 -0.12929 0.84181 -0.033857 0.46164 -0.46674 0.67817 0.25068 0.63897 0.36633 0.39065 -1.0632 1.1472 -0.56069 -0.51291 0.13922 -0.28124 0.98735 -0.1911 -0.59607 0.40367 -0.42682 -gunned -0.040336 -0.63869 0.83447 -0.49077 1.1923 0.073141 -0.70261 0.21779 -0.8618 -0.67178 -0.18952 -1.903 -0.7158 0.69713 0.16494 -0.48371 -0.48875 0.39274 -0.67189 0.83684 0.36572 1.1342 -0.25618 0.63302 -0.58959 -1.0833 0.26343 -0.1755 -0.14619 0.18778 0.24684 -0.29082 -0.55999 -0.35801 0.2266 0.51197 -0.063604 -0.36867 0.15376 1.0113 -0.57478 0.64904 -0.25705 -0.44623 0.98175 -0.98004 -0.72979 -0.78 0.82038 -0.96093 -wrestler -2.2885 0.38612 0.088138 0.78724 0.34599 0.67591 0.69577 0.78103 -0.52315 -0.27944 1.227 -0.076829 -0.39409 0.64538 0.454 -0.68543 0.32815 1.407 -1.1993 0.11591 -1.1287 -0.42833 0.31575 0.52149 0.0015317 -1.2687 0.17826 -0.56839 -0.15229 0.10563 0.74449 -0.21701 0.058808 0.33975 -0.30542 0.79518 -0.11594 0.33254 -0.7672 -0.74689 0.096811 0.24477 -0.46 0.040724 -0.070049 -1.7756 0.31399 -1.2174 -0.39326 -0.04678 -scales 0.15086 0.1876 -0.24394 -0.36085 0.79058 1.3097 0.73409 -0.60276 -0.33468 -1.0964 0.36243 0.23525 0.72989 0.6999 -0.84739 0.16938 -0.10771 -0.56781 -0.2818 -1.5029 -0.49739 -0.637 0.65592 -0.24093 -0.34808 0.42304 -0.1597 0.72839 0.031095 -0.26706 1.6856 -0.20171 0.796 -0.31389 0.66038 0.015301 -0.46144 -0.078821 0.17956 0.036199 -0.3443 0.42697 0.35475 0.25658 -0.27722 -0.64557 1.5545 0.089406 -0.60327 0.2346 -applauded -0.24281 -0.82318 -0.15404 -0.73526 0.29742 -0.80843 -0.67408 0.15305 -0.2992 0.45636 -0.28562 -0.5905 0.11742 0.06859 -0.16161 -0.3842 -0.08613 -0.27961 0.33682 -1.3107 0.037073 0.88135 -0.56687 -0.79076 0.63664 -0.95827 0.24437 0.65237 -1.444 -0.54687 0.77576 0.68407 0.18844 -0.55099 -0.95871 -1.1413 0.28635 0.041898 -0.91904 -0.062634 -0.275 0.29254 -0.43049 -0.083801 0.38705 -0.1477 -1.0938 0.45029 -0.71901 -0.0039061 -rallying -0.70101 -0.32374 0.42944 -0.16814 0.21265 -0.56775 -0.094796 0.37738 -1.0431 0.62636 -0.57266 -1.309 -0.47584 -0.18684 -0.45782 -0.66501 -0.078254 -0.8096 -0.44604 -0.55242 -0.12924 -0.20353 -0.5895 -0.12059 0.25319 -0.62026 0.19537 -0.44533 0.20238 0.22481 1.3453 0.95524 -0.053533 0.13621 -0.66677 -0.80595 -0.55047 -0.21166 -0.71074 -0.25724 -0.28527 -0.44666 -0.29679 0.15602 0.88123 0.45846 0.037432 -0.29323 0.18343 -0.64748 -speculate 1.11 -0.59681 0.40967 -0.1155 0.028529 -0.41354 -0.35837 1.0612 -0.86047 0.2537 0.30285 0.43279 -0.24851 -0.23966 0.95558 1.2279 0.34168 -0.61407 -0.060797 -0.76867 0.10736 -0.31052 0.37369 0.20807 0.99358 -1.325 -1.0931 -0.048916 0.16165 0.10932 0.66917 -0.26327 -0.035717 -1.0073 0.065602 -1.1341 -0.35882 -0.28166 0.078914 0.19213 -0.48442 0.0067983 0.12449 0.85118 0.97852 -0.6235 -0.0015159 1.4945 0.32046 0.25382 -accredited -0.53382 0.6012 -0.24801 -0.6702 -0.34592 -1.2049 -0.61519 -0.33858 1.2746 -0.34749 1.3473 -0.1744 0.96664 0.30325 -0.40831 0.22292 -0.49396 0.84324 0.82173 0.65664 -0.093569 0.70984 0.57972 1.0861 -0.9461 -0.58384 0.42377 -0.65227 -2.078 -0.12623 1.3476 -0.18161 -0.15192 -1.2043 0.82268 -0.15513 0.63318 0.97607 -0.2614 -0.2103 0.20442 -0.88699 1.3576 0.88548 -0.81087 -0.066309 -0.41068 1.1741 -0.77248 0.13271 -exams -0.61445 0.24949 -0.34487 -1.028 -1.0812 -0.28827 0.67736 0.30977 0.41567 -0.038921 -0.20262 -0.99127 0.61651 -0.073913 -0.027454 -0.071879 -0.91784 -0.43443 0.030481 -0.079917 0.70532 0.46939 0.72416 0.36967 0.36868 -0.72657 -0.51582 -0.78615 -1.4558 -0.64948 1.8528 0.93086 -0.88839 -0.72583 1.2548 0.54237 1.3879 1.2043 0.47981 -0.41682 -0.48784 -0.59115 -0.047549 1.7028 -0.3315 -0.89901 1.9124 0.85126 0.687 -0.18295 -regrets 0.35226 0.33406 -0.39729 0.1558 0.13843 0.48969 0.43491 1.5046 -0.015525 -0.3383 -0.24372 0.77082 -0.52705 -0.66284 0.94928 -0.21333 -0.53359 0.12507 0.49745 -0.061752 -0.21869 0.96212 0.26395 -0.54332 1.4502 -1.3169 -0.43161 0.35163 0.43543 0.063932 0.66684 0.6842 0.099349 0.10772 -0.8194 -0.42325 0.5131 -0.74234 -0.41695 -0.14413 0.27499 -0.50571 0.29084 -0.28382 0.011885 0.59701 -0.96694 1.7286 0.01035 0.084128 -4.9 -0.0095322 -0.4711 1.4445 0.32185 0.87206 1.1995 -0.19407 -1.4738 -0.59866 0.3362 0.68998 -1.5543 0.63371 -0.6546 1.1755 0.15348 -0.63321 0.085499 -0.80019 0.35627 0.57537 -0.057899 -0.46499 -1.1274 0.19788 0.86711 0.86944 -0.039006 -0.12175 0.50533 1.4717 0.52076 0.96114 0.82018 -0.14874 -0.7648 -0.27649 -0.3988 1.0659 -0.52739 -0.81937 -0.14957 0.68624 -0.60831 -1.2293 -0.68114 0.36686 0.60824 0.4808 -0.28336 -clout -0.45064 0.061766 0.56609 0.056188 0.55548 0.036727 0.88289 0.48728 0.28742 0.77044 -0.078811 -0.080451 -0.021995 0.12762 -0.89059 0.41095 0.55301 0.18061 0.84985 -0.95503 0.38251 -0.56596 -0.75513 -0.55002 -0.5093 -1.4912 -0.55013 -0.25476 0.28588 0.61954 1.3107 1.6407 0.36246 -0.77374 -0.66418 -0.23417 -1.6423 0.30368 -0.52267 -0.66797 -0.25171 0.6012 -0.72538 0.46687 -0.62964 0.067518 -0.58467 0.65134 -0.96558 -0.14611 -variants 0.72545 -0.086012 -0.080149 0.36711 -0.68307 1.3956 0.031566 -1.2182 -0.37673 -0.16844 0.96029 0.37619 0.37706 0.032504 -0.15545 0.024264 -0.90396 0.47015 -0.31251 -0.48647 -0.85277 -1.1169 0.84634 0.78765 0.10933 0.18167 -0.97699 -0.13659 -0.091661 0.0095194 1.7331 -0.64737 0.1606 0.014001 1.3788 0.48347 0.56562 0.11026 -1.4913 0.62779 0.531 -0.18644 0.014327 0.35533 0.20991 0.23967 0.54393 0.92127 -0.15006 -0.10998 -perpetrators 1.2689 -0.88621 0.13477 -0.5234 0.95584 -0.04912 1.3573 0.14769 0.37975 0.23086 -0.42066 -0.13517 -0.8268 -0.39481 1.2153 -0.43167 1.2656 -0.6383 0.13547 -0.032339 0.05385 0.37839 0.48983 0.32217 -0.24523 -0.89055 -0.58398 -0.69476 0.23502 -0.46827 0.72248 0.32677 -0.9293 -1.8024 0.069439 0.46186 0.52496 -1.42 -0.99058 0.032742 -1.0332 -0.31943 1.0106 0.17355 0.65968 -0.70905 -0.72897 0.40995 1.1261 -0.887 -abkhazia 0.017666 0.099762 0.3565 0.23644 -0.87299 0.16001 0.62781 1.2491 -0.97103 -0.81496 1.2624 -1.5973 0.34507 0.0075307 -0.55105 0.78687 0.34382 -0.070435 0.81455 0.81922 -0.99179 0.53616 -0.14567 -0.043148 -0.067625 -1.0048 -0.26891 0.82075 0.13237 0.10313 1.2301 -0.12342 -1.2494 -0.32654 -0.16961 0.14777 -0.77607 -0.71951 -1.1143 0.43195 -0.36854 0.58968 1.0288 -2.0284 -1.9614 -1.1603 -0.075681 0.82577 -1.1234 -1.491 -unconscious 0.45127 -0.5372 0.14606 -0.569 0.88567 0.81421 0.49236 1.1689 -0.16457 0.28818 0.76197 -0.081765 -0.56276 0.40064 -0.10685 0.17106 -0.81207 -0.21027 -0.063284 -0.049461 -0.5052 1.7829 0.21687 -0.069707 1.0466 -0.68377 0.091899 0.3877 0.82893 0.10926 1.2359 -0.39854 0.40695 -1.137 -0.22137 0.67718 0.50363 -0.23343 1.2931 -0.21454 0.01344 -0.39734 -0.1059 0.95288 0.12276 -0.18264 0.36613 -1.0824 1.1058 -0.79683 -pakistanis 0.26776 -0.99583 1.1118 -0.7552 0.75617 -0.495 -0.25301 0.66154 -0.25356 0.31145 -0.18013 -0.89384 0.60381 -0.26818 0.38193 0.1402 0.43613 -0.23281 0.0419 0.74195 -0.087548 1.4514 1.0584 0.69071 0.60334 -0.91237 0.0077493 -1.2771 0.16551 0.14169 1.1343 1.0015 -0.22231 -0.12104 1.0022 -0.070236 -0.32321 -0.92857 -1.449 0.46746 -0.78319 0.31106 0.81981 0.28573 0.72612 0.033637 -1.3088 0.69187 -0.27728 -1.2317 -saeed 0.020934 -0.42271 0.070219 0.37805 1.1382 -0.98016 0.63418 0.47608 1.1053 0.49758 -0.11528 0.33026 -1.8081 0.27963 0.15564 0.60668 0.27922 -0.33295 0.35373 1.1564 0.57096 1.3173 1.9453 0.63092 -0.75003 -1.4372 0.26667 -0.26958 -0.90286 0.63633 0.22165 -0.79608 -0.31946 1.1064 1.552 -0.12203 0.31325 1.0667 -0.70977 0.61516 0.23056 0.87482 -0.1875 0.037354 1.5929 -0.053506 -0.9488 0.71889 0.59853 -0.45816 -cane -0.79114 -0.77025 -0.63059 -0.11845 -0.049528 0.61825 -0.53564 -0.43757 0.81525 0.37643 -0.12739 0.1631 0.95734 -0.68621 -0.1872 -0.48204 0.30316 1.0559 0.36466 -1.3233 -0.42508 -0.44342 0.57857 -0.55501 0.25258 -0.4714 0.90317 0.59961 0.56433 -0.35025 1.4594 -0.67906 -0.57672 1.1733 0.4553 0.34278 -0.70431 0.57147 0.81947 0.28754 -0.82906 0.35713 -0.70917 0.057595 0.72552 -0.18176 -0.31296 -1.621 -0.15777 -0.8437 -apparel -1.3278 -0.4199 -0.59122 0.79286 0.30206 0.057754 -1.2899 -1.7167 0.83478 -0.012681 0.88714 -0.60127 -0.41624 -0.74612 0.48069 0.47213 -0.9818 0.46556 0.79644 -1.1775 0.60259 0.021217 -1.0904 0.18983 -1.521 -0.089333 -0.88457 -0.083405 0.67165 -0.23437 1.3193 0.9526 0.90651 0.68557 0.33681 -0.20411 -0.70664 0.58408 -0.52021 0.068967 -0.077237 -0.018751 0.64191 0.050259 1.1421 -0.18035 -0.51425 -0.015238 -0.46152 0.52944 -eternal 0.080009 2.1698 -0.48957 -0.40142 1.019 -0.11011 1.1975 0.33966 0.13489 1.0549 0.10426 0.42304 -0.27528 -0.2461 0.25657 0.68603 0.70943 0.29385 -0.10208 0.63668 -0.33226 0.68902 -0.24441 -0.00087731 0.58969 -0.54293 -0.68272 0.24735 1.2196 0.43867 1.034 0.35862 -0.98164 -0.30198 -0.77578 -0.29152 0.17701 -1.5481 -0.19464 -0.3854 -0.0023806 -1.142 -0.68052 0.23778 -0.51612 0.25603 -0.16241 -0.78419 -0.36035 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-residences 1.0582 0.97709 0.29686 -0.25777 0.12327 -0.39829 -0.64248 -0.22206 -0.88436 -0.61866 -0.60775 -0.82552 0.6264 -0.34255 -0.40734 0.26496 -0.30143 0.27994 0.62123 -0.085125 0.57756 0.2464 -0.92568 0.3585 -0.95735 0.60163 -0.025886 -0.16202 0.21204 0.3475 1.5309 -0.12063 0.77117 -0.58666 1.1982 0.95021 0.25311 0.24721 -0.44752 0.479 -0.20027 -0.3325 1.0193 0.82484 -0.61112 -0.12531 -0.9912 -0.734 0.0098026 -0.54559 -vanilla -0.32934 0.40539 -0.40185 0.74612 0.51669 1.1118 -0.22081 0.17886 -0.33832 1.3485 0.68058 0.21251 1.1364 0.31457 -0.17303 0.12289 0.13689 0.38971 -0.01794 -1.1175 0.14654 -0.3226 1.7585 0.2306 -0.81335 0.56118 -0.12962 0.94446 1.9098 -0.11732 1.2301 -0.28942 -1.3114 1.2003 -0.044858 -0.41453 -1.6438 0.33327 1.0593 -1.0497 1.2178 -0.40029 -0.48464 -0.71748 0.32612 0.72842 0.41264 -0.032452 -0.25718 -0.53324 -greenback -0.61486 -0.62284 0.5645 -0.20411 0.11557 -2.1565 0.53772 0.13808 -1.3408 0.57832 0.18735 0.2478 0.53746 -0.045028 -0.34902 0.48333 -0.89558 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0.23693 -0.21452 1.0853 0.0086903 -0.67295 0.66191 -0.051016 1.8615 0.055645 0.35615 -0.13441 -0.48773 0.97638 -1.4111 0.56036 -0.81131 1.0325 0.34142 -0.78573 -0.10304 -0.34449 1.2629 0.06245 0.34075 0.4516 -0.27435 -0.78421 1.4712 0.58807 0.64897 0.18681 1.0544 0.64918 0.1436 0.93099 1.1915 -0.68003 0.28252 0.18545 -0.08766 0.346 -0.9452 0.35117 -0.40445 -disband 0.21827 -1.9186 0.11594 -0.65757 0.40288 -0.76679 0.81094 0.86099 -0.37263 -0.069851 0.47456 0.037496 -0.23858 0.6306 -0.39698 1.0271 0.12853 -0.00079533 0.68343 0.098576 0.45794 -0.90606 0.15107 0.33778 -1.4085 -0.95431 0.16369 -0.44905 -0.28698 -0.17405 0.65805 0.39459 -1.7478 0.096066 -0.17387 0.10895 0.88761 -0.77433 -0.52476 -0.22399 -0.48701 -0.64253 -1.1008 0.048075 0.23798 0.11304 -0.70816 0.21537 -0.39367 -0.14545 -shrouded 0.62107 0.72077 -0.78521 0.089334 -0.47958 -0.56323 0.20412 0.19819 0.50733 -0.57036 -0.14474 -0.67869 -0.20998 0.64078 0.4254 0.98275 -0.82963 0.25195 -0.24685 -0.48077 -0.63006 0.64173 0.027312 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0.60371 0.3714 -0.076045 -0.58431 0.59256 0.69903 0.50774 0.1305 0.85513 -0.19432 -vioxx 1.6086 -1.8364 0.39109 -0.23744 -1.5448 2.0845 0.25382 0.08596 1.4607 0.68928 0.41439 -0.46605 -1.1467 -1.0447 0.99381 0.47211 -1.4924 -0.71442 -0.69357 -0.33739 -0.003768 -0.13347 -0.42437 -0.044339 -1.1226 -1.6443 -0.089087 -0.35697 -0.20904 -0.18892 0.09401 -0.0026363 0.72943 -0.19572 0.24176 -0.81226 0.66796 0.20365 0.27088 -0.64494 0.39939 0.29266 0.34584 1.5593 0.93985 -0.07953 0.54292 0.26697 -1.275 0.69972 -seaport 1.1897 0.027893 -0.74458 0.26774 0.070041 -1.2684 -0.39496 0.43165 0.5929 0.10866 -0.39653 -0.19053 0.39914 0.29469 -1.1446 0.17896 0.70915 0.61126 -0.35618 0.99087 0.97512 0.09848 -1.7447 0.19573 -0.17166 -0.2828 0.16546 0.40354 0.76754 0.053733 0.73886 -0.03986 0.37391 0.60208 0.49392 0.36139 -0.19066 -0.27036 -0.066742 1.3319 -0.14473 0.094831 0.52961 -0.28085 -0.69966 0.69702 -0.59134 0.33883 0.3672 -0.36667 -introductory -0.42521 1.3526 -0.82053 -0.92199 -0.37192 -0.3463 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-0.19499 -0.052308 0.17067 0.132 -0.41241 1.0951 -2.0624 0.5967 -0.66618 -0.1116 0.76301 -0.37934 -quezon 0.65388 0.2363 0.32054 0.90576 -0.18465 -1.2777 0.36595 -0.44336 -0.48216 -0.27154 -0.53731 -0.73256 0.20916 -0.55092 -0.90033 0.28052 -1.0944 0.89913 0.44461 1.1619 0.056952 0.13581 -0.17042 0.10274 -0.7565 -0.43534 -0.075576 0.23374 -0.91471 -1.0799 -0.33953 -0.059313 -0.53274 -1.7156 0.74088 0.21863 -0.33683 -0.43817 0.67566 0.69711 -1.2527 0.70161 -0.13896 -1.126 -1.0011 0.62059 -0.066595 -1.5688 0.72921 -0.11744 -carrillo 1.8435 -1.1013 -0.47783 0.44319 0.54751 -0.38482 -0.063106 -0.1138 0.048825 0.60586 0.40752 0.79189 0.19633 -0.12492 0.48952 -1.2666 -1.0479 0.12443 0.085705 0.286 -0.93004 -0.57642 0.62822 -0.082217 -0.57262 -0.55824 0.67184 -0.095019 -0.75713 -0.47788 -0.34546 -0.12292 -0.54584 -0.65707 -0.2674 -0.14133 -0.73081 0.48131 0.79442 0.37488 -0.69383 0.96613 -0.26923 -0.016182 0.16897 -0.61439 -0.51191 -1.0992 0.98591 -0.059359 -empowering -0.19415 -0.9809 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-0.10066 -0.97142 0.24008 -1.518 -0.81775 -0.73732 -0.44933 -0.45148 -0.24444 -0.60871 0.79077 -0.043544 0.73192 1.5499 0.81463 0.21198 0.084871 0.30132 0.46739 -0.71363 0.32456 -0.2636 0.0010035 0.18309 0.39701 0.9033 -0.15402 -0.47893 -0.10204 1.3522 0.043575 0.72015 -selectively 0.29988 -1.3997 0.46869 -0.70111 0.1106 0.20233 0.21794 -0.48046 0.33519 0.59696 0.47152 0.53238 0.65799 0.20397 -0.24161 0.8915 0.024958 -0.3949 0.75039 -0.31616 0.37729 -0.70859 1.301 0.067318 0.046112 -0.51267 0.20288 0.3763 0.51616 -0.90436 0.74275 -0.42501 0.091047 -0.96641 -0.044891 0.042582 0.085038 0.15029 -0.79849 0.07009 -0.13582 -0.50338 0.15431 0.81492 0.63644 -0.84915 0.073234 0.33677 0.15808 -0.084915 -truthful 0.40898 -0.79578 0.0098288 -0.21384 1.1076 -0.33637 0.62574 0.15239 0.25507 1.0819 -0.90649 -0.0075591 0.33885 -0.50236 0.40773 -0.37753 -0.10682 -0.084934 1.371 -0.23338 0.14676 1.3705 1.0258 -0.36004 1.0439 -0.26344 -0.79476 0.49019 0.62464 -0.46303 0.49083 -0.98157 -0.18686 -1.0131 -0.098589 -0.3143 0.36856 0.75681 -0.93089 -0.72212 0.8472 -0.7254 0.2434 0.99084 -0.75785 -0.73909 -0.45582 1.3925 0.4188 0.95536 -gladly 0.22565 -0.44092 -0.20169 -0.86074 0.29545 -0.22351 -0.17615 1.1419 -0.44524 0.19482 0.081891 0.96718 0.36421 -0.48243 0.78793 0.62218 0.1936 -0.2439 0.66432 -0.45967 0.27012 0.30075 0.15559 -0.15002 0.56104 -0.59806 -0.14405 -0.40832 0.42709 -1.2098 0.096205 1.4076 -0.58386 1.084 -0.21842 -0.40199 0.15918 -0.10828 -0.081943 -0.43665 0.044037 -0.6168 0.060824 0.78207 -0.11628 -0.43545 -0.41974 0.51371 -0.82076 0.22083 -hendricks -0.46323 0.14831 -0.11408 0.48443 0.007034 0.39727 -0.62331 0.28317 -0.35444 -0.26642 0.26193 0.62034 0.17798 -0.22313 0.22794 -0.10693 -0.013527 -0.35788 0.18775 0.32637 -0.40337 -0.34547 0.17075 -0.21157 0.14022 -0.28496 0.46517 -0.056361 -0.40988 -1.0412 -0.38072 -0.29723 0.55156 -0.23777 -0.34981 -0.6777 0.00025427 -0.18169 0.63668 0.47137 0.13077 0.48684 -0.5497 0.10842 -0.31582 -0.80127 -0.0047564 -0.30205 -1.0146 1.1409 -steelmaker 0.21639 -1.2942 0.53184 1.3421 0.1648 0.15307 0.0213 0.785 -0.3679 0.89608 0.91428 -0.62207 0.43328 1.067 -0.92723 -0.14075 0.28175 0.51199 -0.361 -0.42937 0.90337 -1.3513 -0.51887 -0.9074 -1.344 -0.52944 -1.081 0.39389 -1.2364 -0.55783 0.037377 -0.96337 0.8736 2.0237 -0.09386 -1.9256 -1.0429 0.70281 1.0687 -1.2096 0.19189 -0.61013 1.0583 -1.3151 0.28963 0.35585 -0.55646 0.84736 -0.48747 -0.34179 -syndicates 0.89293 -1.5526 -0.36274 0.88958 -0.6598 0.11292 -0.0064334 -0.34635 0.50365 0.24127 -0.048956 0.65411 -0.71521 0.7373 0.52413 -1.2348 0.68868 0.50106 -0.51051 -0.13048 0.61764 -0.50514 0.31074 1.3942 -1.2064 -0.5267 0.38764 -0.74413 -0.28318 -0.27753 0.58434 0.5754 0.65861 -0.043336 -0.40103 0.19058 -1.1192 -0.49986 -1.0509 -0.16187 -0.23548 0.1603 1.132 0.97138 0.23214 -0.81521 -0.69834 0.36151 0.472 -0.15868 -gma -0.1568 -0.73266 0.37435 0.99393 0.60235 -1.3302 -0.85205 -0.55744 1.3549 0.48598 0.32828 0.23534 0.43512 1.3717 -0.29035 -1.1124 -1.4399 0.89327 -0.34064 0.67506 1.1008 0.50752 0.82837 0.49852 0.25626 0.57444 -0.8921 -0.14385 -1.2037 -0.56502 0.48716 0.57416 0.15749 0.075378 -1.0749 -0.34285 -0.37053 -1.0318 -0.3891 -0.73979 -0.068237 -0.059427 -1.694 -1.32 -1.7417 0.20884 0.13119 -0.49992 -0.13304 0.49814 -308 0.44202 -0.85021 0.75453 -0.55026 0.32135 0.44647 0.59759 -0.8858 -0.12164 -0.30934 -1.3014 -0.81456 0.28302 -0.36295 -0.13039 -1.2743 -0.2365 0.50541 -0.4937 -0.10327 -0.27305 -0.55212 0.615 -0.11973 -0.29887 0.76182 0.29981 -0.027684 -0.94242 -0.047363 0.46318 0.38922 0.56889 1.0985 0.68717 -0.19536 0.87468 0.0024 -0.27464 0.059176 -0.5048 -0.48675 0.75862 0.26942 -1.0699 1.2493 -0.89965 0.054871 0.69325 -0.30817 -khrushchev 0.018913 0.46096 -0.16926 -0.57351 -0.66379 0.30073 0.35383 0.37101 -1.8248 0.028721 -0.53566 1.0993 0.28791 0.91308 -0.14692 0.056189 -0.79655 0.351 0.33351 1.2175 0.53654 -0.39119 0.83009 -1.9771 0.88523 -1.2171 0.17748 0.21254 0.51106 0.24932 -0.19277 -0.53262 -0.94489 0.025343 -1.1781 0.24284 0.12121 1.0475 -0.59148 0.49416 -0.57689 -0.29478 -0.26988 -0.49022 0.052644 0.67754 -1.0287 -0.21873 -0.90249 -0.38552 -grail 1.1686 0.39403 -0.63931 -0.64922 0.53944 -0.48577 -0.069571 -0.78849 1.0095 0.92818 -0.0093746 1.2403 -0.63872 -0.094744 0.25075 0.21936 0.80886 0.21945 -0.77057 0.55204 -0.4715 0.098337 0.10739 0.09137 -0.13719 -0.22977 -1.1372 -0.22366 0.10518 0.072701 -0.26205 -0.20965 -0.81934 0.39402 0.038219 0.25911 -0.4608 -0.5273 -0.2513 -0.30887 0.73039 -0.46724 -0.76865 1.1342 0.054892 0.17202 0.76797 0.16545 -0.60288 -0.83382 -rcd 0.17708 -0.56647 0.41187 0.6233 -0.30582 -1.5008 0.31736 -0.30447 -1.5196 0.06599 0.76872 -0.73596 -1.0576 -1.2861 -0.51464 -0.40279 0.39221 0.19978 0.56267 0.0029492 -1.8019 0.22295 0.70285 0.58091 -1.3775 1.0531 0.83396 -0.13605 -0.39584 1.3098 1.4365 -0.081385 -1.7718 0.84647 -0.62541 0.12928 -0.59239 -0.72429 0.26263 0.25733 1.4797 -0.12348 -1.1113 -0.30327 0.08424 -0.1604 -0.48082 0.1952 0.25414 -1.2124 -carvalho -0.13405 -0.54645 -0.23714 0.14652 -0.32012 -0.95583 -0.09977 0.12218 -0.54835 0.82806 1.0424 1.0794 -1.0434 -0.39598 0.36491 -0.85537 -0.040085 -0.82804 0.42657 -0.17672 -1.1279 0.63509 -0.055021 0.15771 0.10429 0.34025 1.4658 0.36511 -0.90424 -0.35879 0.34285 -0.22532 0.067388 -0.27596 -0.22679 0.47581 0.42854 0.5284 1.1269 0.16431 0.56622 0.93486 0.22364 -0.65159 -0.59988 -0.081549 0.16924 -0.44358 1.4663 -0.4594 -1999-2000 -0.68541 -0.32306 -0.4685 -0.51115 -0.62591 -0.37858 -0.27393 -0.98145 -0.11188 0.0042571 -0.15509 -0.47806 -0.48472 -0.27122 1.2503 -0.45575 -0.28125 0.56031 -0.55308 0.8884 -0.022573 -0.50425 0.39408 -0.71611 -0.46096 0.55031 0.15671 -0.09141 -0.53057 0.68895 0.6461 1.147 -0.086477 0.391 0.12152 0.55197 -0.24617 0.74166 0.35436 -0.65566 -1.4232 -0.49836 -0.38508 -0.1262 -1.1342 0.073931 0.71551 0.83753 -0.065339 0.0029554 -disused 0.34886 -0.04278 0.21298 -0.21458 -0.60549 -0.42618 -0.75506 0.50479 0.062291 -0.59089 0.30333 -0.35701 -0.18064 0.18863 -1.0488 0.80019 0.56056 1.2915 0.077912 0.14885 0.81331 -0.51947 -0.73204 0.70936 -0.40868 -0.12348 0.55249 1.1392 0.82821 0.32859 0.90296 -1.8565 0.38242 0.19733 0.29556 0.47768 0.50671 0.61182 -0.10706 0.65067 0.6011 -1.4221 -0.063401 0.63628 -0.32611 -0.14967 -0.55874 -0.58664 -0.065336 -1.2892 -crocodiles 0.8987 -0.59262 -0.37313 -0.13372 -0.30624 -0.024743 -0.31819 -0.31789 0.45871 0.206 0.21989 0.58971 1.5722 -0.23286 0.18876 0.67713 1.0634 0.78952 -1.5842 0.78648 -0.83249 0.20154 1.3925 -0.017312 0.39273 0.19674 -0.0309 0.25622 -0.08195 -0.73668 0.2504 0.24464 0.82927 0.25743 0.042614 0.67427 -0.30032 -1.0817 -0.047513 -0.53574 -0.74162 -0.52226 0.58133 1.3188 1.1816 -0.065456 -0.32079 -0.39772 -0.032599 -1.0139 -overshadow 0.37357 0.23446 -0.44346 0.46177 -0.46129 -0.038541 0.46204 0.77029 0.031733 0.20616 -0.61632 0.20595 -0.22675 0.31103 0.69636 1.0943 0.27047 -0.70932 0.79778 -0.4632 0.038173 -0.46055 -0.15014 -0.0079655 0.66464 -0.4097 -0.28429 0.20187 0.50832 0.32653 -0.013872 1.3117 -0.40427 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-trondheim 0.17453 0.67395 -0.63252 -0.44094 -0.59668 0.62962 -0.4653 0.75956 -0.49767 -0.3111 1.1165 -0.84539 -0.093238 -0.22355 -0.62911 0.50279 0.51015 1.1751 -1.0624 1.0355 1.2108 0.012722 -0.27578 1.1162 0.033956 -0.083126 0.045042 0.18415 -0.29621 -0.87506 -0.072594 0.65524 -0.24666 0.23186 -0.13331 -0.51638 0.51435 0.34745 0.41574 1.5404 0.84694 -0.80381 -0.23455 -0.69406 -1.3756 0.13533 0.18802 0.50126 -0.76861 -0.47277 -westphalia 0.32536 -0.53046 -0.11421 -0.95221 -0.43774 0.19222 0.22047 -0.15412 -1.0116 -0.81874 0.06417 -1.0162 -0.40467 -0.97445 -0.4594 1.1307 0.84363 -0.41849 0.51101 1.9207 0.24923 -1.3774 0.20139 0.42964 -0.28549 -0.8196 -0.9065 0.095908 -0.56128 0.64283 -0.13261 0.26602 -0.35432 1.0429 0.08553 -0.81044 -0.80789 1.154 0.52371 1.2006 -0.30264 0.077212 0.24407 -0.85201 -1.1146 0.86871 -0.24156 0.59036 0.33969 -1.2931 -uterus 1.11 0.61318 -0.1674 -1.067 0.24571 2.7003 0.54305 0.21418 0.91976 0.1143 1.2456 -1.0673 1.0355 1.1356 0.42899 1.6262 -0.55248 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-unsealed 0.29435 -0.85814 0.22873 1.1776 -0.15658 -0.23359 -0.21079 0.80902 0.16755 -0.31868 -1.2828 -1.0533 -0.29336 -0.54073 1.0482 -0.28055 -1.6885 -1.2501 0.90513 -0.28781 1.1143 -0.29756 0.49824 -0.15045 -1.6534 -0.70983 0.68678 1.3368 -0.17411 -1.0441 -0.093687 -2.4784 -0.19818 -1.2519 0.60308 -0.090303 0.92033 -0.019657 -0.072966 -0.17554 0.065305 -0.17812 0.50765 0.21116 -0.3408 -0.10393 -0.36589 0.23371 0.46705 -0.90514 -departmental -0.07254 -0.33034 -0.86826 -0.1892 0.092741 -0.58901 0.75695 -0.89225 -0.0027297 -1.4048 -0.39995 -0.018266 -0.67146 0.0031422 -0.39905 -0.5053 -0.54743 0.39532 1.1096 -0.45739 0.72799 -0.50006 -0.11835 0.22691 -1.4282 0.16715 0.32688 0.47015 -0.98641 -0.13851 1.1559 0.74792 0.066551 -0.60912 -0.028156 1.0568 0.0889 0.064445 0.087768 0.64044 -0.20344 -0.45203 0.11701 0.63128 -0.83833 -0.42859 0.15454 1.0448 0.88545 -0.36387 -lanier -0.64814 0.83329 0.008024 0.35021 -0.61279 0.64671 -0.37403 0.61545 -0.14596 -0.1417 -0.33005 0.22131 0.34065 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-0.43743 0.051159 -0.51422 0.39534 -0.24073 0.22367 -0.078666 0.98726 0.72056 -0.53254 -0.71153 -0.68199 0.44307 -1.0716 -0.63432 -0.055806 0.38259 -1.1467 0.40681 -0.34339 -0.40937 -0.41769 -0.74872 -0.46388 -0.65067 -0.6343 0.62092 -1.2741 0.91576 -1.5211 0.15011 0.89094 -0.7931 0.85739 0.34594 0.36754 1.7291 0.27125 0.92912 -0.12783 0.079872 -0.81947 1.6192 -0.77637 -0.77565 0.33347 -0.82469 -carolinas -0.49067 -0.49004 -0.11864 0.19351 -1.9907 -0.78393 -0.70274 0.84217 0.58302 -0.96551 -0.16841 -0.93867 1.0185 -0.69224 -1.4014 0.58662 -0.91819 -0.15827 -0.42963 0.27201 -0.30964 -0.13152 0.39287 -0.24364 -0.13095 -0.15083 -0.11731 0.23214 0.57245 -0.069162 0.60413 0.58625 0.10565 -0.60026 0.12706 -0.066738 -0.91088 -0.1053 0.2607 0.038759 -0.56307 -0.16025 0.2276 -0.42604 -0.25846 1.2183 0.44373 -0.33957 -0.98654 0.05642 -fonseka 0.24105 -1.993 0.83563 1.3686 0.39333 -0.87655 0.86542 0.38332 -0.31007 -0.2312 -0.28599 -0.60963 -0.54165 -1.1508 0.080532 -0.18214 -0.63161 0.17381 0.54569 -0.11061 -1.304 1.0575 0.58956 -0.4041 -0.29809 -0.20591 0.34383 -0.51416 -0.76133 1.3472 -0.26469 -0.23413 -1.2546 -0.11286 1.067 0.33156 -0.12072 -0.2699 -1.2029 -1.2907 -0.21471 0.87085 -1.0182 -0.28388 -0.083305 -0.8637 -0.813 -0.59621 0.95076 -0.14208 -inference 1.0452 -0.90168 -0.71306 0.28089 -0.52452 0.85734 1.2002 -0.32204 -0.3818 0.7232 0.31641 0.29374 -0.35432 -1.3266 -0.61641 0.044217 0.32101 -0.26709 -0.29008 -1.2185 -0.78957 -0.11745 -0.094824 0.4993 0.86605 -0.1512 -0.77284 0.32607 0.29259 0.20692 0.76866 -0.8501 -0.81079 -1.0897 0.29507 -0.85703 0.50712 0.15961 -0.16928 0.18234 1.0089 -0.43248 -0.15603 1.4574 -0.83295 -0.43339 1.0549 1.6499 1.5527 0.18843 -shrinks -0.2843 -0.61376 1.0926 -0.66223 0.82086 0.29046 0.3502 -0.27999 0.35797 -0.23887 0.63877 -0.56695 0.81008 0.43908 0.29709 0.52042 -0.42208 -0.19108 0.23247 0.46678 -0.15808 -1.2077 -0.2311 -0.23303 0.38666 0.12178 -0.1212 0.13117 0.68283 0.67306 0.19027 0.9615 0.71587 0.34965 -1.0073 -0.052403 -0.82821 0.44122 0.38232 -0.99966 -0.42466 -0.24021 0.50546 -0.48418 -0.28523 -0.23157 0.95827 -0.069088 0.2161 0.27225 -pathogen 1.4587 -0.4627 0.019051 0.58265 0.34404 1.0382 0.95494 -0.52771 1.2972 0.22632 0.63856 -0.23977 0.80345 -0.38296 0.13258 0.24706 0.27706 0.22072 -0.41943 0.24521 -1.1967 -2.3077 0.48078 0.28858 0.44187 -0.41595 0.50384 0.23434 1.0577 0.43664 0.22865 -0.57691 -0.24208 -1.6845 -0.74515 0.21516 0.065525 0.74934 -0.047107 0.45465 -0.044775 -0.53629 -0.39578 0.28693 1.7856 0.66366 0.36091 0.20448 0.50572 0.51932 -danilo -0.031792 -1.0737 -0.56634 0.63043 1.2747 0.17267 0.28187 -0.28297 -1.0095 -0.87661 0.903 -0.71845 -0.90677 -0.2039 0.63297 -0.81476 -0.080616 -0.0092178 0.19449 -0.26665 -0.96257 -0.72229 -0.16607 0.74339 -0.17848 0.16307 0.51353 -0.020125 -1.263 -0.16085 -0.55725 0.55332 -0.68502 0.94583 0.5031 -0.22046 -0.20625 1.0393 -0.20108 0.73635 -0.10065 1.1947 -0.64567 -1.5524 -0.011651 -1.5023 0.60651 0.072762 0.75082 0.46992 -kyung -1.3734 1.549 1.1019 0.71045 -0.72891 -1.0706 0.54684 -0.23789 -0.084518 0.19153 -0.42964 0.0025464 -0.26528 0.12073 -1.4578 0.3335 0.65534 0.18025 0.10344 1.7401 -0.90018 0.58638 1.1196 -0.99798 0.71551 -0.75202 0.53016 -0.028287 -1.6305 -1.1277 0.6609 -0.069653 0.60726 1.4251 0.27134 -0.54516 0.79441 -0.12286 -0.2826 -0.67349 -0.26067 -0.72048 0.19255 -0.81765 1.8703 -1.6765 0.069069 -0.64019 0.46309 -0.016261 -osha -0.9058 -0.45312 0.068313 -0.36585 -1.5472 0.14735 0.18614 -0.80812 0.34401 -0.81965 0.37287 0.51625 -0.55915 -0.42676 0.97534 0.11636 -0.23037 0.29109 0.74729 -0.8279 -0.59562 -0.39333 -0.36841 -0.83786 -0.46534 -0.61473 0.29139 0.41789 -0.59854 -0.41642 -0.24075 -0.55066 -0.17482 -0.54396 -0.24218 -0.28523 0.72434 -0.50964 1.4437 0.67708 -0.52786 0.010322 1.3251 0.61493 -0.026946 -0.73094 0.37197 1.7684 -0.21967 0.80744 -stranglehold -1.271 -0.92123 0.13872 0.37245 -0.55033 -0.42724 0.28143 0.47581 0.31399 0.42937 0.24223 -0.75253 -1.259 0.62572 -0.52617 0.69617 0.22947 -0.41969 0.71857 0.12796 -0.062122 0.20918 -1.2207 0.28029 -0.68628 -0.70756 -0.022679 0.017298 0.6924 0.76723 0.67965 0.68471 -0.044291 0.74227 -0.76235 0.24659 -0.85408 0.020792 -0.57693 -0.58837 -0.043678 0.068922 -0.18952 0.77913 -0.71031 -0.31973 0.16402 0.035289 -0.33037 -0.99724 -tebow -1.1302 -0.60623 0.58245 0.246 -0.00036121 0.4237 -1.2116 -0.22517 0.25431 -0.085245 -0.67564 0.31813 -0.20111 -0.61267 0.99509 0.26766 0.25609 0.25728 -0.1956 0.079836 -1.46 -0.37228 0.37645 0.0058231 1.1764 -0.56835 0.37316 0.12747 0.12286 -1.2893 -0.37887 0.60078 -0.0084647 -0.34756 0.78593 0.67132 0.3345 0.17933 0.49376 -0.60634 -0.87766 0.53356 -1.5051 0.85353 -0.46703 0.78505 0.95284 0.7546 0.18069 0.095094 -lyman 0.17039 0.32987 0.17203 -0.59362 0.040633 0.79507 -0.7795 -0.49478 -0.71472 -0.43985 -0.52849 0.82366 0.060246 -0.34453 -0.42856 0.28466 0.27417 0.62947 -0.33229 0.3997 0.25816 0.074213 -0.26891 -0.34307 0.81049 -0.075253 0.75959 -0.31104 -0.83835 -0.35849 -0.92072 -0.9989 0.13074 -0.94994 0.38667 -0.52421 0.19291 -0.058074 0.65962 0.12581 -0.49802 0.0029328 -0.88579 -0.20537 0.32959 0.10216 0.33998 -0.37761 -0.44003 1.0816 -slocum -0.49486 -0.25369 -0.019439 0.47078 -0.043978 -0.94326 -0.40375 -0.48407 -0.30116 -1.0163 -0.5387 0.70507 -0.6328 -1.5177 -0.037376 0.36819 0.26798 0.62494 -0.6514 0.16563 -1.7343 0.68558 0.22121 -1.2864 0.90085 0.11676 0.40316 -0.023545 -0.016874 -0.018028 -0.11387 -0.33927 0.1471 0.30731 0.18728 -0.14416 0.17958 -0.14675 0.38075 -0.47017 -0.21075 -0.74924 -0.4712 0.97133 0.34786 0.11542 0.29223 -0.29493 -0.50397 0.94965 -hobson -0.24043 0.28917 -0.19769 -0.51949 -0.070331 0.35136 -0.79253 0.23499 -0.35344 -0.30761 -0.39691 0.9364 -0.33878 -0.26297 -0.52031 -0.060164 0.67231 -0.5241 0.020385 -0.10107 -0.22999 0.058419 0.19034 -0.21124 -0.15972 -0.062877 0.16178 0.25647 0.44506 0.18049 -0.79342 -0.87305 -0.49089 -0.52572 0.82873 -0.47314 0.11318 0.3194 0.69862 0.27998 0.082339 0.37161 -0.25442 -0.66491 -0.40989 0.27335 -0.23208 -0.18326 0.097237 1.2268 -siddiqui -0.0035624 -0.18384 0.24556 0.03061 0.040278 -0.24232 0.6518 0.099458 0.93091 0.40295 0.40917 0.21851 -0.44174 -0.74124 -0.11405 0.84 -0.49561 -0.35634 0.62899 1.1074 -0.035997 1.5348 0.98867 -0.15105 -0.57976 -1.0598 -0.54946 -0.16333 -1.1145 0.28646 -0.51361 -0.11875 0.16573 1.0998 1.2128 -0.28571 0.40198 0.14788 0.49061 0.41995 0.44913 1.2686 -0.30894 0.80144 0.20181 -1.5806 -0.2356 0.15481 0.13322 -0.32431 -buoy 0.20107 -0.091046 0.73974 -0.47093 0.2168 -0.65909 -0.61709 0.19644 0.59805 0.053238 0.47339 0.85965 1.1306 0.92967 -1.5157 0.37514 -0.65909 -0.6684 -0.53388 -0.60525 0.57571 -0.81579 -0.26914 -1.2118 0.71371 0.12928 0.46957 0.82738 0.77753 -0.10332 0.46178 0.65072 0.66148 0.13495 -0.14094 -0.22051 -0.22194 -0.34583 -0.14792 -0.96608 -0.10203 -0.97027 0.1517 -0.32178 0.40857 -0.51488 0.67922 -0.04505 1.2308 -0.40572 -sulphur -0.32563 0.97085 -0.098962 -0.36854 -1.0187 -0.012573 0.1685 -0.070348 0.62657 1.2938 -0.0094168 -0.021122 1.516 -0.6408 -0.59203 0.56354 0.63 1.0453 -0.56373 -1.0022 0.4777 -0.79204 0.86229 -0.44151 -0.83096 -0.4281 0.47584 1.6769 0.61265 0.38028 0.10848 -0.77849 0.016378 -0.34337 1.605 -0.50165 -0.32617 -0.75958 0.8063 0.46174 -0.34992 -0.11747 0.31577 -0.65947 0.12708 0.15995 -0.211 -0.50277 0.1739 -0.059078 -unrwa 0.17723 -0.52936 -0.026829 -1.3907 0.21718 -0.37251 -0.29727 -0.19823 1.4648 -1.1524 0.24591 -0.33198 -0.14039 -0.92084 0.38734 0.19945 1.4001 0.37496 1.2318 1.2187 0.027879 0.15903 -0.50879 0.58229 -0.44571 0.38418 0.97764 0.010983 -0.65014 -0.13056 1.0853 -0.13596 0.45164 0.49688 -0.6665 1.2174 1.3485 -1.1037 0.72415 1.353 0.51139 0.29068 -0.16817 -1.1633 -0.14087 0.22813 -0.72433 1.5841 -0.89034 0.46103 -gallant -0.10988 -0.084024 -0.84096 -0.24913 0.8555 0.083108 0.37651 0.58667 -0.019863 -0.56163 -0.26789 0.5347 0.0043245 -0.49326 -0.34468 -1.366 0.22367 0.43657 -0.86812 -0.025814 -0.41595 0.38141 -0.077291 -0.85896 0.33679 -0.029827 -0.5143 -0.68761 0.45929 0.29662 -0.094151 0.65008 -0.00060225 0.18693 -0.15677 0.62652 0.1411 0.047185 -0.57731 -0.57573 0.47184 -0.51369 -0.35625 -0.24486 0.63322 -0.72376 -0.4595 -0.32584 0.20829 1.0466 -19.2 0.054782 -0.42012 1.6751 0.049396 0.44923 0.98176 -0.33741 -2.1071 -0.28713 0.55799 0.63338 -0.91632 -0.021347 -1.0744 0.34493 0.20489 -0.7357 0.23379 -0.33231 1.1511 0.39077 0.5574 0.94613 -0.3362 0.060734 0.79229 0.65484 -0.18568 -0.11008 -0.032344 0.21752 1.2041 0.56183 0.89517 0.60252 -0.73532 -0.70971 0.43041 0.046259 0.46689 -0.17372 0.22946 0.62801 -0.30843 -1.8262 -1.111 0.19713 0.36734 0.62066 -0.51598 -rectory 0.32403 0.8848 -0.97888 -1.2767 1.1007 -0.1133 -0.66445 0.11075 -0.45954 -0.73291 -0.74702 -0.071237 -0.0029129 -1.1635 0.12564 0.12062 -0.73053 0.21508 0.62342 0.75236 0.21467 0.28766 -1.1128 0.44267 -0.6478 0.095187 0.69941 0.52014 0.19488 0.27533 0.2994 -0.99397 0.33269 -0.36417 0.61452 1.0655 0.92371 0.333 0.38235 0.055292 0.4189 -0.69934 -1.0785 1.085 -0.76574 0.75244 -0.5115 -0.90834 0.0077604 -0.93335 -rubinstein -0.5341 -0.11758 -0.86101 1.1902 -0.023419 0.73144 0.49568 1.1342 -1.2196 0.27512 0.33271 0.054774 0.27887 -0.16715 0.19488 0.24623 0.31279 -0.17259 0.58033 0.15775 0.86055 0.20753 -0.3124 -0.66618 0.31429 -0.19572 -0.70037 -0.80667 -0.60971 -0.70441 -0.17707 -0.5449 0.014415 0.30748 0.64464 0.10071 0.89026 0.82364 0.80834 1.2979 0.44248 0.65675 -0.38967 -0.64038 0.18322 0.043808 -0.62976 0.63444 -0.52027 0.72626 -3.00 -0.10012 0.76191 0.10233 0.85565 1.6975 -0.47985 0.692 -0.074006 -1.5162 -0.10365 0.33461 -0.30914 1.1978 -0.054163 0.31584 0.91586 -1.2685 0.10894 -0.782 0.65658 1.0244 -0.13526 -0.79545 -0.5349 0.0885 1.2891 1.3169 -0.26174 1.2203 1.9872 0.67999 0.65368 1.0644 1.7184 0.73092 -0.24536 0.19172 0.55414 0.54395 -0.31294 0.31457 -0.75184 0.16535 1.0949 -0.49357 -0.4665 -0.023561 -0.19977 1.2659 0.33122 -yokosuka 0.46429 0.65865 0.011035 -0.34342 -0.63479 -1.4362 -0.46831 0.60116 -0.29896 -0.5229 0.61989 0.46683 0.39555 0.5387 -0.60367 0.64463 -1.4649 0.31395 -0.99358 2.3435 0.67236 -0.79346 -0.35992 0.037123 -0.72818 -1.2868 1.398 0.46828 0.95236 -0.92614 -0.13806 0.83074 0.22928 0.47294 1.3748 0.038856 0.17345 0.35295 -0.54365 0.20011 -0.41036 -0.45651 1.1467 -1.2804 0.28797 -0.65613 1.2091 -0.2283 0.089839 0.87398 -trulli 0.32594 -0.83853 0.12262 0.95636 1.3043 1.0525 -0.32934 -0.12659 -0.46836 -1.1066 0.62407 -0.33638 -1.9504 -0.60259 1.3022 -0.77663 -1.3318 -0.023202 0.2332 -1.2408 -0.94751 -0.56835 -1.0846 1.2071 1.0633 0.37004 1.9599 -0.49531 -0.14269 -0.72978 0.31701 0.47008 -0.4396 0.90289 0.4568 -0.35906 0.67473 1.226 -0.38591 0.29977 0.096448 -0.71873 -0.10745 -0.46926 0.70149 0.5 1.1094 -0.77606 1.2803 -0.3104 -first-ever -1.0916 0.50406 -0.77839 0.90479 -0.35203 0.0082468 -0.38449 0.47751 0.83115 0.37366 0.27162 -0.51205 0.026296 0.57057 -0.20906 -0.86635 -0.25071 0.14524 -0.87382 0.46647 0.017032 -0.45746 -0.24641 0.058886 -0.32092 -0.14365 -0.51563 -0.44225 -0.41718 -0.020714 0.51145 0.63365 -0.50565 -0.44945 0.034854 0.49471 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0.078948 -watermelon -0.11169 -0.73634 -0.87099 0.66481 0.8636 -0.078829 0.52128 0.18377 0.21471 0.37623 -0.46969 -0.66462 0.948 -0.17327 0.661 -0.58227 0.079968 0.95237 0.60184 -0.4136 -0.23314 -1.007 1.3132 0.3262 -0.65398 0.40491 -0.60377 1.0947 0.58395 -1.0347 0.54564 0.10824 -0.33077 1.1553 -0.266 0.84132 -1.0607 0.7356 0.84014 -0.81504 -0.47628 -0.23533 -0.81993 -0.40683 0.98664 0.045194 0.4074 -0.41 0.22201 -0.25359 -wheelchairs 0.34159 -0.05894 1.1731 -0.6361 -0.95899 0.40597 0.07492 -0.49846 0.73048 -0.1765 0.037165 -0.55218 0.77078 0.015766 -0.42762 0.14483 -0.91133 1.0362 1.3143 -1.6154 -0.42364 0.89069 -0.47463 0.010835 0.86068 0.37704 0.79296 -0.33967 0.36531 -0.81238 0.64423 1.6075 -0.59775 0.29638 0.12688 0.83664 1.2416 0.37908 -0.47766 0.38527 -0.10163 -0.5053 0.47344 1.3333 0.71437 -0.12664 -0.40573 -1.4492 -0.58635 -0.48963 -provisionally 0.5717 -0.7439 -0.084086 0.02763 0.13345 -0.029313 0.040962 0.2683 0.54716 0.2213 1.1828 0.1785 -0.41364 0.90459 1.1604 0.13178 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0.14864 0.16214 -0.4507 0.12893 0.72676 -0.070973 -0.73931 -0.30036 0.68896 1.1562 -0.36383 0.34216 0.56355 -0.49007 -0.66541 -0.060639 0.42938 0.49346 -suresh -0.081123 -0.89037 -0.18956 1.8715 0.56202 0.63005 0.93932 -0.45452 -0.12515 0.0014032 0.26211 1.056 -0.80063 1.4439 0.13266 -0.19508 0.81122 1.1095 -0.79889 -0.12678 0.59017 1.0788 0.4615 0.046123 1.6043 0.48751 1.3175 -0.89909 -1.7028 -0.022691 0.11472 -0.053083 0.4184 0.58037 0.044087 -0.14354 0.60968 -0.3337 0.089223 1.1945 -1.1499 0.83816 -0.59605 0.46829 -0.3121 0.051685 -0.19165 -0.64292 0.73609 -0.11179 -rochus -0.48787 -1.1573 -0.88491 -0.22609 -0.60399 0.5244 1.3879 1.3703 -0.32252 0.34624 0.23629 -0.71621 -0.73474 0.03252 0.51383 -0.56427 -0.24662 -1.6952 -0.58134 -0.29919 -0.77745 0.19086 -0.46092 0.52685 -0.41902 0.023565 -0.014642 -0.92493 -0.060213 -0.15875 0.022795 0.94347 0.64186 1.4363 0.17634 -0.0082554 0.83531 0.84505 0.47467 -0.53815 1.777 1.2347 1.2761 -1.1609 0.58868 1.4168 0.29492 -1.3025 -0.59108 0.55486 -eradicating 0.55725 -0.78937 -0.44614 -0.68892 -0.46848 0.60679 1.0266 -1.6272 1.6045 0.82115 -0.15536 -0.55457 -0.94741 -1.4554 0.27533 -0.57677 1.0894 0.48535 0.65379 0.91862 -0.88908 -0.21581 0.49141 -0.22619 0.098566 -0.021921 0.62779 -0.17875 0.98936 0.47597 0.53966 0.95262 -1.0499 -0.55344 -0.9031 0.37186 -0.91264 0.57406 -0.6241 0.013702 -0.61562 -0.9261 0.022253 -0.13582 0.65666 0.095858 0.26369 0.22663 0.48249 -0.64514 -abdulaziz 0.058712 1.603 -0.3016 0.24274 0.57933 -0.46548 -0.13156 -0.2876 0.79054 -0.051876 0.21143 0.78575 -0.015045 -0.43404 -0.45527 1.0567 -0.42818 -0.24068 -0.19114 2.2695 0.49565 -0.040434 1.1364 -0.64003 -0.91284 -1.478 -0.13679 -0.0357 0.064063 0.22251 -1.3785 1.0746 -0.15854 0.59531 1.1563 0.18181 -0.17552 1.288 -0.37493 1.9626 0.57184 1.1208 -0.40782 0.12748 0.41307 -0.083361 -1.2396 0.36541 0.20301 -0.40588 -speculations 0.64047 -0.49116 -0.54905 -0.068459 -0.39957 0.19696 0.073145 -0.23559 -1.0919 0.7021 -0.58932 0.65734 -0.17052 -0.64742 0.72136 0.37079 0.021492 -0.61724 -0.39766 -0.19312 0.51974 -0.0062421 0.6774 -0.59539 0.76744 -0.19606 -0.15305 0.41896 0.29119 1.1357 0.28125 -0.20097 0.30003 -0.58672 -0.54406 -0.26685 -1.0654 -0.55097 -0.70692 0.084223 -0.051766 -0.031415 -0.55135 1.016 0.51676 -0.22972 0.037386 1.6689 0.41421 -0.99957 -auc 0.84656 -1.296 0.055797 -0.17615 0.72313 -0.72048 0.55941 -0.92035 -1.1305 -0.020562 0.77142 -0.87451 -0.60603 0.051572 0.077874 -0.67501 -0.26494 0.51771 1.0866 1.1498 -1.6686 -0.39307 0.044634 0.27856 -2.0237 0.11262 0.91867 -0.52766 -0.59798 -0.15529 0.51647 -0.66704 -1.4909 0.16713 -0.68702 0.18299 -0.77848 -0.50457 -0.2793 0.4033 0.27337 -0.3375 0.21449 -0.18872 0.33038 -0.46573 0.090986 1.1119 -1.5122 -0.24887 -brendon -0.18935 -0.47457 -0.23398 0.11489 0.99496 0.58026 -0.7252 0.015039 0.73916 0.26367 -0.023603 1.3976 -0.64563 0.72915 -0.5017 0.23339 1.4877 -1.2177 -1.0128 -0.50972 -0.42439 0.9116 0.050587 0.14154 0.26666 1.0432 1.73 -0.53851 -0.44371 0.034437 -0.051755 -1.3285 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1.1804 -1.6442 0.39505 0.2721 0.094999 -0.82426 0.72623 -0.44452 0.0081629 0.87455 0.32554 -1.2253 0.87194 -0.24695 -0.31917 0.88167 0.35055 -1.4918 -0.11075 1.1219 -1.2845 -0.3327 -0.53272 -0.71618 0.50872 0.079926 -0.70377 -0.64088 -0.10065 -0.5421 0.44175 0.85395 -0.35487 0.18858 -0.22533 0.59203 -0.3431 -0.26261 1.2381 -0.92906 0.0048735 0.17811 -0.59403 -0.013094 -0.60976 -0.76283 0.76206 -summarize 0.43526 -0.17597 -0.18935 -0.084276 -0.01105 -0.55908 0.6053 0.28923 -0.085102 0.23721 -0.62439 0.43215 -0.11684 0.05943 0.23719 0.047935 -0.10612 -0.72762 1.0247 -0.35054 0.4941 -0.063308 0.78166 0.20573 1.2266 0.36546 -0.987 -0.34187 -0.24521 -0.64942 -0.14011 0.019961 -0.54277 -0.73288 0.2569 0.068723 -0.35042 0.57013 -0.11743 0.28122 0.023759 -0.52508 -0.054088 0.33406 -0.2607 0.32665 1.4078 1.1051 0.14521 -0.15357 -melons 0.23878 -0.8139 -0.36244 -0.41123 0.14928 -0.54952 -0.14826 -0.50523 -0.53881 0.69103 -0.074686 0.1355 1.1635 -0.61024 0.7631 0.26457 0.41939 1.1542 0.64847 -0.68922 0.017551 -0.73337 1.6127 0.14877 -0.20427 0.99693 0.012801 0.97139 0.45239 -0.70576 -0.041104 0.47957 0.26592 0.5185 1.0053 0.97461 -1.5572 0.68089 0.48861 -0.54587 -0.86367 -0.075665 0.18203 -0.7918 1.2026 0.1799 0.71175 -0.0063251 -0.11755 -0.59925 -618 0.79946 -0.32549 0.48385 0.30308 -0.25887 -0.004843 0.60519 -0.60908 -0.68865 -0.65672 -0.95372 -0.32957 0.53065 -0.9944 0.16855 -0.8955 -1.569 0.33945 -0.16694 1.2685 -0.2725 -1.1854 0.54631 -0.8906 0.36344 0.57889 -0.27542 -0.8097 -0.24007 -0.19665 0.031277 -0.075192 1.0232 1.8376 0.33044 -0.23981 0.26312 0.86556 -0.43538 0.75034 -0.38947 -0.4474 0.59624 0.68687 -0.71793 -0.15441 -0.30184 -0.24642 -0.39518 0.046954 -talmudic -0.99397 0.39919 -1.2906 -1.1312 0.40965 -0.19182 0.37373 -0.01661 -0.75503 0.41164 -0.31161 0.1782 1.1689 0.43305 -0.44281 -0.040162 0.10877 -0.41221 0.27498 1.325 0.52876 0.029969 -0.37321 -0.15184 1.0282 -0.61896 -0.68889 -1.0359 -0.90089 -0.30396 0.49078 -1.2047 0.66529 -0.40141 0.16444 0.44531 -0.07679 0.26241 -0.051261 1.4189 -0.15148 -0.24523 0.12703 1.8511 0.080917 -0.28183 0.95617 1.6306 -0.97358 -0.42636 -retaking 0.45982 -1.3924 0.12434 0.0037012 -0.079601 -0.22869 0.58428 0.774 -0.13083 -0.2968 -0.4465 -1.1776 0.26306 -0.29663 -1.291 0.55234 0.27772 -0.45902 0.25095 0.59782 0.28233 0.0057935 -0.34505 -0.76584 -0.0099519 -0.22908 -0.12891 -0.15183 0.40122 -0.19812 0.19276 0.34285 -0.8759 -0.32382 0.17598 0.21962 -0.4323 0.31478 -0.49956 -0.26309 -0.70969 -0.32192 -0.42522 -0.10638 -0.79864 -0.46507 0.37582 -0.13279 0.1334 -0.54916 -kazuhiro 0.55493 -0.75005 -0.0072796 1.0392 1.7282 -1.1465 -0.50892 0.35061 -0.30493 -0.2805 0.37997 1.2566 -1.7412 1.1044 -0.45286 -1.1776 0.23523 0.1448 -0.40398 1.1663 -0.10085 -0.13157 -1.1555 -0.8141 0.29877 0.57421 0.66346 -0.88876 -0.23953 -1.0469 -0.34869 0.47811 1.2028 1.0068 -0.36192 -0.55601 -0.6833 -0.44217 0.65966 0.6206 -1.0598 -1.5007 0.20426 0.32878 0.45002 -1.0588 0.70246 1.5213 0.49133 0.68837 -1996-2000 0.39789 0.17913 -0.25129 0.32112 -0.58329 -0.24831 -0.1059 -1.0694 -0.26267 -0.34004 -0.04045 -0.020399 -0.046634 0.83404 0.53483 -0.58562 -0.17605 -0.10473 0.44516 1.7912 -0.53645 -1.9493 -0.65755 -1.0768 0.45319 0.99129 0.93862 -1.497 -0.84928 0.058592 1.2386 0.2756 -0.59348 1.7092 0.16917 0.4072 0.192 1.013 0.32114 1.0962 -1.9489 -0.52229 -0.0078133 -0.16282 -0.64174 -0.28268 1.3447 0.67732 -0.5637 0.25738 -dayne -1.9645 -0.060738 1.049 -0.07589 -0.89946 0.73882 -1.3569 0.014382 0.29234 -0.11885 -0.75117 0.13208 -0.8426 -0.81477 0.22422 -0.71027 0.61946 0.58871 0.58967 -0.74603 -1.07 0.048798 0.033524 -0.67129 1.1516 0.045513 0.53525 0.468 0.43986 -1.2525 -0.85282 -0.14823 -0.17994 0.38515 0.23746 0.30949 -0.031772 0.67044 0.45085 -0.15279 -0.26675 -0.08917 -0.76406 -0.28394 -1.0911 0.1979 -0.079568 0.27179 -0.44047 -0.14335 -888-603-1036 -0.56572 0.74134 1.3913 1.0807 -0.77462 0.14715 0.73 -2.0368 -0.20832 0.8706 -2.3526 -0.26738 2.6829 -1.2578 1.0457 -0.53066 -1.9032 -2.0234 1.4868 -0.72033 2.0123 -0.69007 -0.3355 0.47559 0.98639 0.244 0.75144 -2.231 1.1715 -0.8788 -0.065208 0.47346 -1.8494 -0.70774 -0.59373 0.039899 1.1773 -1.0159 -2.339 0.33723 2.7672 0.77767 0.81109 1.8961 0.16064 0.22824 0.5016 0.86833 0.45685 0.79167 -mathilde 0.55917 0.54626 -0.78726 -0.041249 -0.89443 1.9121 1.034 0.40784 -0.60429 -0.029313 0.67988 0.30607 -0.0030482 -0.18652 0.46537 -0.18918 -0.6498 -0.88845 -0.39394 0.077042 -0.50409 0.15124 0.32761 0.029437 0.71942 -0.19019 -0.33252 -0.20468 -0.64335 0.40822 -1.2971 0.76113 0.050363 1.0119 -0.1007 -1.1022 -0.088499 0.73745 0.23122 -0.39924 0.86105 0.5423 1.0562 -0.79421 -0.32955 -0.6909 0.36491 -1.3808 0.39703 0.17042 -unambiguously -0.38468 -0.67579 0.19061 -0.50491 0.79233 0.060671 0.9275 -0.15921 -0.02436 0.26317 0.49631 -0.053677 0.11603 0.35142 -0.14177 0.60094 0.26791 -0.56358 1.0167 -0.029327 -0.84914 -0.45336 0.51167 0.29446 0.95724 -0.65843 -0.44304 0.70882 0.026458 -0.71528 -0.19855 -0.31284 -0.080188 -1.0078 -0.48249 -0.79008 0.56388 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-0.56754 0.1399 -0.53868 -0.56855 -0.21978 -0.66033 1.0697 0.22311 0.44392 -1.1486 -0.38044 -0.47383 -0.56934 1.613 -0.14533 0.81712 0.044565 -0.76151 0.89652 0.55645 0.25429 -0.13396 0.27662 -0.4259 -0.60649 -0.26554 1.623 1.0582 0.68484 -0.85661 0.41549 -0.52075 -peseta 0.049018 -0.8874 0.28366 -0.46169 0.11018 -1.2943 0.90219 0.13957 -1.2644 0.91163 0.56892 0.36392 0.59981 -0.36992 0.27563 -0.75117 -0.42171 -1.0808 -0.44235 0.76206 -1.4403 -1.1433 -0.98331 -0.82146 -0.18273 0.10715 1.1019 -0.13281 -0.34898 0.56633 1.1589 -0.41567 1.1785 1.9709 -0.86941 -0.57213 -0.013011 -0.78192 -0.20554 -1.1133 0.63719 0.38037 1.0869 -0.094556 -0.64792 0.0081996 -0.0090865 0.060872 -0.14958 -0.6956 -detergents -0.010039 -0.78312 -0.38223 0.67689 -0.62805 0.26919 0.63889 -1.2304 0.26867 0.40534 1.1519 0.65124 0.58626 -0.21576 -0.28909 1.3879 -0.65 0.94805 0.29128 -0.5645 1.2637 0.5273 0.94887 0.22589 -1.4284 0.59232 -0.51519 0.28827 1.0988 0.31353 -0.13543 0.90962 0.51139 -0.519 0.47921 1.1958 -0.55414 0.24466 0.85018 -0.018901 0.84715 0.18848 1.0227 0.17671 0.87436 0.92128 -0.35252 0.019328 0.92752 0.10622 -gar -1.0014 -0.84068 -0.31374 0.29312 -0.40785 -0.53279 0.54532 -0.4109 -0.42393 -1.0655 0.38977 0.25075 0.5705 0.21492 -0.40517 0.5015 -0.034679 0.58437 -0.14707 0.16928 -0.92993 -0.043818 0.56496 0.30169 0.036789 -0.0069465 -0.32267 0.13658 -1.0133 -0.85369 -0.39492 -0.52744 0.5764 0.7434 -0.61015 0.38534 0.3723 -1.2161 1.0543 0.28824 0.39711 0.25182 -0.2983 0.64033 1.4445 -0.14205 0.24641 -0.68067 -0.07093 -0.080093 -co-starring -0.13715 0.38895 -0.64725 -0.05023 -0.25798 1.0009 0.35405 -0.21209 -0.3187 0.25785 0.030146 0.92945 -0.62243 0.77151 0.058582 -0.4561 0.37828 0.45084 -0.17817 0.6173 0.44961 0.4674 0.37396 0.49052 0.23723 0.40859 0.12115 -0.31024 -0.30736 0.1596 0.076741 -0.12089 0.61317 -0.36013 0.28357 0.73682 -0.090308 -0.13988 -0.97471 -0.48129 -0.11256 0.86068 -0.60754 -1.6251 -0.33997 -0.63972 0.404 -1.2038 -0.21651 1.0329 -dreamers 0.30796 -0.47556 0.061177 -0.73473 -0.24871 -0.65756 -0.28864 -0.60375 -0.8229 1.0946 -0.29684 0.35797 0.091705 0.80244 0.26313 -0.26645 1.2865 0.51361 -0.024811 0.062317 -0.68637 0.70451 0.18549 0.52063 0.35368 0.74541 -1.1273 -0.51898 -0.082077 -0.37158 -0.52404 0.1067 -0.72426 0.15409 0.037585 0.10718 -0.4496 -0.69336 0.017669 -0.45717 -0.61109 -0.54535 0.36461 0.65876 0.18061 0.083804 0.13142 -0.067975 -0.018299 -0.38793 -mistral 0.12377 0.63578 -0.33852 0.34077 -1.2294 0.1691 1.1423 0.64129 -0.62408 0.059179 0.2141 -0.077666 0.23339 0.04572 -0.47196 -1.4549 -0.43064 0.93088 -1.2435 -0.92648 -0.056888 -0.10878 -0.60989 -0.66437 0.50909 -0.54868 -0.5466 -0.5211 0.1965 0.1964 -0.29645 0.053108 -0.5131 1.5073 -0.077393 -0.59876 -0.25342 0.47953 -0.51017 0.52489 0.99926 -0.42895 1.7943 -0.26314 1.5413 -0.506 0.27572 -0.13591 -0.44961 0.14538 -cafu -0.52398 -0.66357 0.17019 0.3577 -0.0015802 -0.95238 0.074386 -0.59393 -0.52948 0.39679 1.3744 1.1225 -0.74204 0.0021425 0.28679 -0.5309 -0.46264 -0.24638 -0.009079 -0.70555 -2.7322 0.23457 -1.056 0.38501 0.16317 -0.12836 1.5482 -0.15014 -0.80971 -0.32083 0.01341 0.74729 -0.31386 0.59736 0.0031265 0.76398 -0.1009 0.028955 -0.13085 0.047588 0.4492 1.3325 0.49349 0.7808 -0.76736 0.66711 0.92876 -0.33809 0.37887 -1.6888 -544 0.25108 -0.60213 0.58676 -0.30492 -0.010253 0.053577 0.63147 -0.47116 0.07526 -0.096005 -0.92223 -0.83634 0.42482 -1.052 0.23144 -0.987 -0.63664 0.43038 -0.15262 0.61698 0.039599 -0.25107 0.66888 -0.12753 -0.059561 0.76438 0.2866 -0.27801 -0.30947 -0.31587 -0.044556 0.2559 0.81954 0.92254 0.41033 -0.22076 0.29784 -0.13819 0.0057876 0.31033 -0.33457 -0.6259 0.85088 0.22682 -0.84886 0.6084 -0.23476 0.46891 -0.0083199 -0.21862 -electrocution -0.099349 -1.1113 0.23068 -0.13654 -0.84628 1.6572 1.4652 0.78315 0.25509 0.79813 -0.35241 -0.64822 1.3546 0.42116 0.57584 0.063412 -0.45554 -0.035417 -0.45178 -0.16261 0.0042942 -0.29874 -0.032007 -0.21404 0.72327 -0.93274 -0.0067467 -0.34604 0.88401 0.19855 -0.5157 -1.2411 -0.76295 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